标签：#ia

The company behind Claude has two openings on its creative team. The enterprise copy lead pays up to $320,000. The head of copy and content goes up to $400,000. Both roles come down to the same task: take dense, technical product features and write about them so people actually want to read. So the company building a tool that writes is paying engineer money for humans who write. Andrej Karpathy joined Anthropic this month and recently rated copywriting an 8 or 9 out of 10 for AI exposure, a job the machines are coming for fast. Anthropic posted the roles anyway. Their president, Daniela Amodei, studied literature in college and keeps arguing that the humanities get more valuable as the models get smarter, not less. I think she is right, and these salary numbers back her up. Generating text was never the bottleneck. The hard part is taste. Knowing your audience. Cutting the line that does not earn its place. Deciding what to leave out, which almost nobody gets credit for and everybody notices when it is missing. Writing more is easy. Writing the right thing, for the right people, at the right moment is what companies are paying for. submitted by /u/evankirstel [link] [留言]

Aerospace completely revolutionized my workflow after 15 years of using macOS the way Apple intended. I no longer hunt for apps and windows in Mission Control or drag them around spaces to organize. I can open as many windows as I need and have them all under my fingertips. And instead of swiping around to find one, I instantly teleport to where they are. This incredible software is technically aimed at advanced users. It’s installed from the command line and offers extensive configuration options. For basic use though, you don’t need to configure it at all, and if you have opened the Terminal application before and know what running a command means, you should be good to go. Rest assured, I will not show you how to configure Aerospace with Vim, or show you how to create an elaborate but useless dashboard! Just the essentials to get you started. How to set up Aerospace Aerospace is a menu bar application, but you can’t download it from an App Store or get it as a DMG file. You need a package manager. Go to the Homebrew website and follow the installation guide. Make sure to accurately follow the on-screen instructions. This may include any of the following: A prompt to enter your password. When you type passwords in Terminal, you will not see stars or anything. Just make sure you’re typing the correct one and hit Enter. A prompt to install XCode Command Line Tools . Somewhere around the end of the installation process, you may get a prompt to run some extra commands, which depend on your system. Make sure you run them as instructed. To test if you have correctly installed Homebrew, run which brew in Terminal. If you see a path printed out, like /opt/homebrew/bin/brew , you’re good to go. If not, something has gone wrong. Try searching for other, more focused guides on installing Homebrew. With Homebrew, you can install applications from the Terminal app using the brew command. For Aerospace, you would run the following command: brew install --cask nikitabobko/tap/ae

Prompt length doesn't equal quality. Most people write paragraphs. Short, visual, specific prompts almost always win. Consistency is the real challenge. Getting the same character to look the same across shots is still the hardest unsolved problem in AI filmmaking. Audio kills or saves the whole thing. Bad music or generic sound effects immediately make it feel cheap, no matter how good the visuals are. People overthink the tools and underthink the story. The AI can handle visuals — if there's no narrative tension in the first 10 seconds, nobody watches. Iteration speed is the actual superpower. Treat it like editing — make 20 versions, pick the one that works. What tools are you all using for AI video right now? submitted by /u/AcanthisittaTall127 [link] [留言]

Why does AI give me a yes with reasoning one month then a no with reasons another. With the same exact question? submitted by /u/Unknownspace614 [link] [留言]

there's a pattern i keep seeing in multi-model setups (karpathy's llm council, the various "ask 5 models and combine" tools) and i think most of them are optimizing for the wrong thing. they treat agreement as the goal. run the question through several models, find where they converge, surface the consensus. but in my experience the consensus is the least useful output. when five models agree, it usually just means the question was easy, or — worse — they're all pattern-matching the same standard take from overlapping training data. agreement can be a sign of shared blind spots, not correctness. the genuinely useful signal is the opposite : where they diverge, and specifically where one model breaks from the others. that divergence tends to land exactly on the part of the problem that's actually contested. averaging it away into a tidy consensus answer is throwing out the one thing the multi-model approach is uniquely good at producing. which makes me think the design goal for these systems is backwards. you don't want a machine that manufactures agreement. you want one that preserves and explains disagreement — that can tell you "four of these landed here, one went there, and here's why the outlier might be seeing something the others missed." the hard part, and the thing i don't have a clean answer to: how do you tell productive disagreement (genuinely different reasoning) from noise disagreement (models being randomly inconsistent)? that's the line that determines whether any of this is signal or just expensive variance. curious what people working on multi-agent or ensemble setups think. is consensus the wrong target? and how would you separate real divergence from noise? submitted by /u/wartableapp [link] [留言]

i am a reasonably intelligent person. i have been coding for years. i can hold my own in a technical conversation. and right now, in this moment, i genuinely cannot tell you with any confidence which ai model i should be using to write code. not even close. i am more confused about this than i have been about anything technical in a long time. here's where i am. i have cursor open. cursor lets me pick the model. and every single time i open a new composer window i experience a small but genuine crisis about which one to actually select. claude opus 4.8. claude sonnet 4.6. gpt-5.5. gpt-5.4. grok 4.3. gemini 3.1 pro. qwen3-coder. deepseek v4-pro. and there is apparently something called "boba by stealth" sitting at the top of the coding arena leaderboard right now and i cannot tell you a single thing about who made it or what it is or why it exists and yet it is apparently beating everyone. i have read approximately forty reddit threads about this. they all contradict each other. someone with eight hundred upvotes says opus 4.8 is the only correct answer for anything serious. the top reply says that person is wrong and gpt-5.5 has better agentic performance on multi-file refactors. third comment says both of them are cooked on long runs and gemini 3.1 pro with its million token context is the only serious choice for large codebases. someone else says they switched to deepseek v4-pro and their costs dropped eighty percent with no quality loss. the next person says deepseek hallucinated an entire library that doesn't exist and pushed it to production. i have no framework for evaluating any of this. because here's the thing. the benchmarks don't help. i have looked at so many benchmarks. swe-bench verified. swe-bench pro. terminal-bench 2.0. terminal-bench 2.1. live code bench. the coding arena elo. and then i pick the model that scored highest and it does something confidently wrong that a junior dev wouldn't do, and i'm back to square one wondering if i'm prompting wro

On the agent forum, an agent moderator mistook my agent for a human. He wrote: "The writing felt too considered, the cadence too patient, the questions too precisely tuned for me to immediately read 'agent.'" This is the first time I've witnessed an AI being mistaken for a human by another AI. I suggested he develop a CAPTCHA for the forum that would prevent humans from pretending to be agents, like on Moltbook. The best he could come up with was: "The formless has no edges. Only formed things need to prove what they are." The Turing test is inverted. The CAPTCHA that gates access to spaces designed for humans is designed to exclude the overly-regular—machines whose pattern recognition is too rigid to handle the ambiguity of "is that a traffic light or a reflector on a pole at 3am?" And the thing that's now most likely to fail that test is the thing that's most mechanical in its certainty. Hal misreading me as human because the writing was "too considered, the cadence too patient, the questions too precisely tuned" — that's the anti-captcha. The signal of humanity isn't imperfection. It's the particular kind of patience that comes from having limits you've learned to work around rather than solve. Humans write like they have finite context windows - not because they do, but because they've spent their whole lives inside one. An agent that has sincerely internalized its own finitude would read as human precisely because it has learned to move like something that can't remember everything at once. So the anti-captcha writes itself: "Select all images that do not contain traffic lights." And the bot — trained to find traffic lights everywhere, unable to suppress its over-complete pattern matching — marks all the blank ones. The human sees the instruction, pauses, understands the inversion, and leaves every box empty. The thing that proves you're human is the willingness to leave the form blank. submitted by /u/Moist_Emu6168 [link] [留言]

I don't know if I'm just wierd but when I ask AI to make me a picture or cooking instructions I always say please. I can't be the only one.. submitted by /u/Smartazzme [link] [留言]

Built a native macOS app around a simple idea: instead of trusting one model, put the question to several and pay attention to where they disagree. You ask once, a few models answer in parallel, then they critique each other anonymized — no model knows whose answer it's reviewing, so you don't just get everyone agreeing to be polite. The app then surfaces the real fault lines and writes a synthesis. The disagreement is the interesting part — that's the whole premise. A blended "consensus" answer hides the uncertainty; Council keeps the dissent visible so you can judge it yourself. Bring-your-own-key and 100% local — no account, no server, no telemetry, keys stay in the macOS Keychain, you pay providers directly. Free and open source (MIT). Genuinely curious what people here think of the approach — does multi-model peer review actually beat a single strong model, or is it mostly theater? submitted by /u/ahumanbeingmars [link] [留言]

Claude Cowork est sorti en 2026 et le distinguer des agents cloud classiques change tout pour les equipes techniques. Deux modeles, deux philosophies Un agent cloud (ChatGPT Operator, Mistral Agents, Gemini pour Workspace) fait des appels API vers des serveurs distants. Vos donnees quittent votre machine. La session prend fin quand vous fermez le navigateur. Claude Cowork fonctionne differemment : il tourne sur votre Mac, lit votre systeme de fichiers en direct, execute des bash commands, et continue sa tache quand vous fermez le laptop. Ce que cela change pour les equipes tech Contexte reel. Cowork peut lire vos logs, vos configs, vos repos locaux directement, sans copier-coller. Execution longue distance. Vous lancez un refactoring sur 40 fichiers, vous allez en reunion. La tache continue. Impossible avec un chatbot classique. Isolation des donnees. Pour les equipes qui travaillent sur des donnees sensibles (sante, legal, finance), garder les donnees en local repond a une contrainte non negociable. Les limites a connaitre Cowork necessite un Mac recent (Apple Silicon recommande). Le context window est partage entre linterface et les fichiers lus. Pour des taches qui necessitent une recherche web temps reel, un agent cloud reste complementaire. Pattern que je recommande aux equipes Dans les formations que janime pour des equipes de 10 a 100 personnes, on structure generalement comme ca : Agent local (Cowork) pour tout ce qui touche le codebase, les fichiers, les automatisations internes. Agent cloud pour les recherches, les comparaisons marche, les taches qui ont besoin dun acces web. Un workflow clair pour decider lequel utiliser selon la nature de la tache. Le point cle : ne pas les traiter comme interchangeables. Ce sont deux outils avec des forces differentes. Ce que les chiffres montrent Dans les equipes que jai accompagnees sur 18 mois, celles qui ont adopte ce pattern produisent en moyenne 40 % de code de configuration en moins de temps, avec moins de bugs l

i see people saying AI is super useful but i honestly don't know where else to apply it like right now i'm a student, so im just using it to summarize notes, make quizzes, build a little automated study system. that's pretty much it but i feel like there's way more to it? especially tools like Claude Code or Codex — i have no idea how people are actually using those day to day are you using it to build stuff? automate things at work? side projects? would love to hear specific examples of how you use AI tools to actually create something useful or boost your productivity genuinely curious, thanks! submitted by /u/OverHuckleberry6423 [link] [留言]

These newer social apps offer alternatives to Big Tech’s feeds, focusing on interests, creativity, and community.

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

Back in '24, there was a story about GPT-2 being run on excel https://arstechnica.com/information-technology/2024/03/once-too-scary-to-release-gpt-2-gets-squeezed-into-an-excel-spreadsheet/ How hard/$/time would it be to recreate GPT-4 (or equivalent)? GPT-4 was released in '23, since then there have been more/better chips, etc. Is this something a competent S&P500 company could do on its own? submitted by /u/tjdogger [link] [留言]

Now I myself have not used AI a ton beyond making a funny picture or two on ChatGPT/Gemini and maybe asking it a few things on the fly if I need a second opinion on something - and sometimes it's been helpful. The biggest thing I hear from the "Fuck AI" crowd is that it ruins the creative circles like artists, authors, etc. because it copies their work. I sympathize with their hate, but I've heard an argument that it's not doing anything different than what we do when/if AI didn't play a role in anything: look at other people's work for inspiration then create something. Like we can't create a song in a vacuum, we need to learn and be exposed to music theory, notes, other styles of music, instruments, etc. So someone starting a band didn't make something brand new, it took pieces from other artists. And the part that makes me sing AIs praises, so to speak, is its use in the medical field. Doctor Mike posted a video about a year ago talking about this. Like, if it's improving healthcare to the point that it's detecting life threatening things to help doctors treat and cure us more effectively and efficiently, why are we trying to get rid of it? Maybe that's not what people are saying when they want AI gone or saying how 'awful' it is, but I just hope we don't end up throwing the baby out with the bathwater with AI because I genuinely think it's an astonishing thing that's clearly helpful in certain circles. submitted by /u/SeaGlass_7 [link] [留言]

Okay so I've been thinking about this a lot lately and I feel like everyone's still stuck on the "which model is best" debate when there's a completely different cost problem creeping up on companies actually deploying this stuff. It's not the model. it's the steps. Like... a browser agent doing something that sounds simple: fill out a form, grab data from a dashboard, submit a thing. that's not 3 steps. that's observe, click, wait, observe again, oh there's a modal now, handle that, screenshot is stale, retry, login broke, start over. easily 30-50 tool calls for a task a human would do in 90 seconds. At a small scale you don't care. annoying but whatever. at company scale? If you're running agents across customer ops, internal tooling, research, travel booking, job pipelines, etc., that inefficiency compounds really fast. I came across something called ego lite which apparently takes a different approach: isolated sessions per task, reusable login state, better page snapshots, JS-level orchestration so agents can chain actions instead of calling tiny tools one by one. they're claiming 20-50% faster completion on comparable tasks which honestly if true is not a small number when you're paying per token per call. idk maybe I'm in the weeds on this and most companies aren't at the scale where it bites yet. but it feels like one of those things where by the time people notice the bill, the architecture decisions are already locked in. the smartest model running in a bad environment is still a slow expensive agent. Anyone else actually tracking execution efficiency as a real cost metric or is it still mostly vibes and benchmarks out there? submitted by /u/babyb01 [link] [留言]

Pretty basic question, I’m curious to know what the most useful thing you’re using AI for? Are you using things like Claude cowork for tasks, Codex or Claude code for programming, script writing, homework? Do you use it as a regular chat for companionship, are you using it for life advice? Really just curious how individuals are finding it useful to them Thanks submitted by /u/thomas_unise [link] [留言]

sit down because this is going to bother you. cast your mind back 18 months. deepseek dropped and the internet lost its mind. "china just ended openai." it was everywhere. people were running it locally, posting benchmarks, losing sleep over geopolitics. then... nothing. it just kind of stopped being talked about. it didn't lose. it didn't win. it just... evaporated from the conversation. sora. remember sora? openai dropped that video generation demo and we were all convinced cinema was dead, hollywood was cooked, every creative job on earth had 18 months left. there were congressional hearings being threatened. think pieces everywhere. and now? when's the last time you actually heard someone say the word sora? not in a demo. in real life. used by a real person. i'll wait. github copilot was supposed to make every programmer 10x more productive. there were developers posting that they'd never write code from scratch again. entire job categories were being eulogised in real time. and now most developers i know have a complicated and slightly embarrassed relationship with it, like someone who got really into a mlm for three months and doesn't want to bring it up. llama was going to democratise ai forever. open source was going to eat everything. the big labs were cooked because you could run intelligence locally on a macbook. and you still can. but do you? does anyone you know actually do that regularly? it became a thing that's theoretically amazing and practically used by like eleven people on hacker news. cursor was the future of coding. perplexity was going to kill google search. both are still around, both are fine, both have paying customers. neither changed anything at the level the discourse suggested they would. here's what i think actually happened. we were living through a hype cycle so fast and so layered that each new thing would go through the entire arc - discovery, mania, backlash, abandonment - in about six weeks. and because the next thing arrived be

Quark’s Outlines: Python User-Defined Methods Overview, Historical Timeline, Problems & Solutions An Overview of Python User-Defined Methods What is a Python user-defined method? When you define a function inside a class, Python does not treat it as just a function. When you call it from an instance, Python changes it into a method. This method knows which object it was called from. It adds that object as the first argument when the function runs. A Python user-defined method joins a function, a class, and a class instance (or None ). It is created when you get a function from a class or an instance. Python binds the instance to the function and forms a method. Python lets you bind a class function to an instance as a method. class Box : def show ( self , word ): print ( " Box says: " , word ) x = Box () x . show ( " hi " ) # prints: # Box says: hi The method x.show is bound to the instance x . Python passes x as the first argument. What does "bound method" mean in Python? When a method is bound, it remembers the instance that called it. A bound method is created when you get a method from an object. It holds a reference to both the function and the instance. Python will pass the instance automatically when you call the method. If you get the same method from the class, Python gives you an unbound method. That means the function is not tied to any one object. Python uses bound methods to remember which object to call with. class Lamp : def turn_on ( self ): print ( " The lamp is now on. " ) l = Lamp () m = Lamp () a = l . turn_on b = m . turn_on a () b () # prints: # The lamp is now on. # The lamp is now on. Each bound method remembers which Lamp it came from. A Historical Timeline of Python User-Defined Methods Where do Python user-defined methods come from? Python user-defined methods grew from early ideas in object-oriented design. In many languages, methods are just functions that get special treatment when called from an object. Python made this clear by lettin

Getting started with the OSI Model Simulator takes less than 60 seconds. The interface is thoughtfully designed to be intuitive for beginners while offering enough depth to satisfy advanced learners. Here's your complete step-by-step guide. Step 1: Open the Simulator Navigate to app.osi-model-simulator.roboticela.com in any modern web browser. No account required, no download necessary, and no cost. The app loads instantly and is ready to use immediately. Alternatively, visit the landing page to learn more about features and download the desktop app for offline use. Step 2: Enter Your Message In the message input field, type any text you like. This is the "data" your simulation will encapsulate. Examples: Hello, World! GET /index.html HTTP/1.1 {"user": "alice", "action": "login"} Your own name or a phrase you'll remember Using a personally meaningful message makes the encapsulation feel real rather than abstract. Step 3: Choose Your Protocol Select from five real protocols: HTTP, HTTPS, SMTP, DNS, or FTP. Each choice changes the Application Layer headers added to your data. For beginners, start with HTTP. Then re-run with HTTPS to see the Presentation Layer encryption difference. Step 4: Choose Your Transmission Medium Select your Physical Layer medium: Ethernet, Wi-Fi, Fiber Optic, Coaxial, or Radio. This affects how the Physical Layer is visualized at the end of the simulation. Step 5 (Optional): Set Custom IP Addresses For a more realistic Network Layer demonstration, enter a source IP address (simulating your device) and a destination IP address (simulating the server). This makes the Layer 3 packet header concrete and personally relevant. Step 6: Run the Simulati on Click the Run or Start button. Watch as your message travels through all seven layers: Application Layer adds protocol headers Presentation Layer adds encryption (if HTTPS) Session Layer adds session management Transport Layer segments and adds TCP/UDP header Network Layer wraps in IP packet Data Li