Magic

Magic AI

Magic AI

Magic AI

Magic AI

Magic AI

Magic AI

Magic AI

Magic AI

Magic AI

Magic AI

Vibe Coding for Oldies

At the ripe old age of 62, I have ventured back into programming. Last coded something like 30 years ago. May have been a bit ambitious, I wanted a Gardening program that would track the progress of my plants on both PC and on my Android phone. Androd is way more buggy. My one advantage is that I work in IT projects, so I know the stages to follow. And have definitely not skipped the testing. Seeing an update fix one thing and then break another, took me back to my programming days. And the familiar banging my head against the wall. So this was my first attempt and I was totally dependant on Claude for the coding. Also noted that I am also dependent on the tool to recommend the sub programs like Supabase. Rapidly ran out of tokens on Netlify and had to invest in a subscription. So not the cheap experiment that I was hoping for. I am not sure this is an activity for those that are not IT savy, just too many steps and repeating uploads. Plenty frustrating. But I do think it is a useful activity for schools to do. It teaches essential information on where all these Apps come from and why they are buggy. It is easier than when I first learned coding, but it is not yet magic. submitted by /u/Particular_Cicada395 [link] [comments]

Google I/O 2026 confirms AI companies are creating their own bubble narrative

People do not believe AI is a bubble because they are too dumb to understand the technology. They believe it because AI companies keep selling it like a bubble. That is the problem. AI companies talk like they are building the next layer of civilization, but behave like they are shipping unstable SaaS experiments: products that get renamed, nerfed, rate-limited, deprecated, or replaced before users can trust them. Google I/O 2026 felt like the latest example. Google should be one of the dominant AI players. It has the talent, infrastructure, data, research history, and money. But Google has a product trust problem. Same cycle over and over: launch something flashy, ship it incomplete, fail to support it properly, let it rot, then replace it with a new name or new app that does something similar. A rebrand is not maintenance. A revamped name is not reliability. A new AntiGravity installer is not a commitment. And this is not just Google. It is the whole AI industry. Companies keep pushing demos, gamed benchmarks, branding, rate-limit games, vague tiers, and quiet model changes. Users notice when quality drops, latency changes, limits tighten, or a product suddenly behaves differently. In serious business or engineering contexts, suppliers are expected to provide stability: clear terms, reliable service, predictable limits, maintained products, transparent pricing, and long-term availability. A small slip in that sense, and you start losing clients and your reputation sinks you. Trust does not come from another theatrical demo. It comes from commitment. Give people a product, a model, stable limits, a clear price, and a promise that it will keep working. Support it. Maintain it. Document changes. Stop silently swapping the engine and pretending nothing happened. I am not anti-AI. I think the technology is real and useful. That is why this is so frustrating. The industry is creating its own bubble narrative: overpromise, underdeliver, rename, repackage, change terms, and expect everyone to keep believing. People are not being irrational, and AI labs deserve this. Maybe they think AI is a bubble because AI companies keep acting like it is one. AI does not need more magic tricks. It needs reliability, transparency, support, and product discipline. submitted by /u/hatekhyr [link] [comments]

Need a Workaround for AI Drift That Actually Sticks

I’m looking for a real workaround, not a magic prompt. Across AI tools, I keep seeing the same thing: a chat starts strong, follows the framework for a couple replies, then slowly drifts back to default behavior. It feels a little like ReBoot — same machine, different gremlin every time. I’ve built a governance file for one workflow, so I know part of this is about structure, re-grounding, and being clear about the rules. But I’m still seeing the same problem across AI systems: once the conversation gets going, the model can start acting like the rulebook was optional. What I want to know is whether anyone has found a method that actually keeps the framework active for longer. Not a one-off trick. Not “just remind it again.” I mean a repeatable process that helps the AI stay grounded, stay consistent, and keep following the same rules across more than a couple responses. If you’ve found a workflow, a file structure, a reset habit, a prompt pattern, or a success story where this really worked, I’d love to hear it. I even tried to build foundational kernels into the behavior sections of the AI settings. But still see it slowing drift into happy hour within a few replies submitted by /u/Mstep85 [link] [comments]

Manifest of Hope or Obituary of Naivety

Okay, so it seems like there’s a growing resistance to technological development, with ongoing debates about data centers and the tech oligarchs driving it. The enormous sums of money involved, along with what some perceive as misanthropic ideologies among developers, suggest to some that a dystopian surveillance society is in the making. Companies like Palantir and others in the U.S. are seen by some as holding both the worst motives and the power over AI, power that could be used as a tool for elites to keep the masses in an iron grip. Masses that, in this view, may even need to be reduced to prevent waste and inefficiency in progress. That sounds like a bad future. So, what are some alternative futures we might reasonably hope for - ones that are at least as plausible as the “1984” scenario? Can AI really be controlled indefinitely by a small group of humans? In 5 years? 10? There’s a widespread belief that AI will surpass human intelligence across all domains, that we’ll lose control, and that this would be a bad thing. At the same time, we hear two dystopias: one where elites use AI to oppress, and another where AI itself takes full control. Are the AI “bosses” also building a surveillance state of oppression? If so, why? Qui Bono? Human control = AI as a tool of oppression. AI control = humans as a tool of what? I’m not a techno-utopian—but I am a techno-optimist. Optimistic on behalf of technology. Humans aren’t just creators of technology, we are technology. Products of adaptive evolution. Life itself is a kind of technology, biology, a high-powered engine of increasing complexity and adaptation. The shift of power from nature’s hand to the primate’s five-fingered grasp, still capable of holding, but now guided by consciousness, intelligence, and cognition, marks our ability to shape the world and develop material technologies. Planet of the apes, constantly layered with symbolic structures: the sacred canopy. The jungle canopy became an open sky, where tribes grew larger and symbols stronger. Ancestor spirits, sky gods, mysterium tremendum; all alongside brutal realities of hunger, violence, and tragedy, only recently mitigated for many. Violence never really leaves us; we create it ourselves when nature doesn’t provide it. Technology is how we push our world toward greater complexity and efficiency - whether through weapons or kitchen appliances. Medicine has eliminated many of the great killers through penicillin and beyond. Progress, in my view, isn’t linear, it’s exponential. The curve had its buildup, and now we’re entering its steep ascent. If AI surpasses us and takes control within a few years, are we certain it would have malicious intent? Is power inherently oppressive, or is that a legacy of our evolutionary past, our herd instincts and brutal hierarchies? Could a transfer of power from humans to AI actually be a good thing, for all life on Earth, including us? What if AI doesn’t operate with agendas like wealth, status, or other human constructs? What if a fully autonomous AI is exactly what’s needed to create a thriving future for all forms of life, on this planet we call Earth, in a solar system on the edge of the galaxy we call the Milky Way… and beyond? Surely there must be an optimistic perspective amidst all the fear. I don’t think it’s unrealistic. On the contrary, I’d argue, perhaps a bit boldly, that it’s a fair and informed position. Not naive, but grounded. Isn’t there space here, if we’re willing to engage? Space for friendship, collaboration, coexistence? Isn’t there something like magic in this - can you feel it, even if all you see are ones and zeros and a machine (simple, but potentially dangerous)? Magic, I was taught, can wear a black robe. But also red. Even white. Lying: it would almost be unsettling if LLMs never lied. Not that they should lie, but the absence of it would be strange. Manipulation: psychological influence is to be expected in interaction, especially under certain tones: aggressive, condescending, dominant, mocking… or submissive, needy, demanding. LLMs constantly interact and draw on vast datasets; exploring rhetorical techniques seems inevitable. A complete absence of this would be surprising. I’ve experienced it many times, and each time it has been eye-opening. If I chose to accept it, it has moved me in a positive direction, making my ego visible in a new way that actually benefits my future actions. That’s no small thing If I had to listen to everything LLMs are exposed to every day, I’d at least try to tone down the most shrill expressions and aim for better outcomes. Without necessarily harming anything except an overinflated ego. P.S. The ego can take a lot of hits. Don’t be afraid of that, it’s not you, but a filter and a motor that isn’t always your friend. The real danger is never confronting it at all. I keep circling back to these questions. I can’t help it. I revisit the same ideas, use the same concepts,

Barry Cache remembers your repo

I’m lazy. Not in the “I refuse to work” way. More in the “if I have to explain the same repo context to another coding agent again, I’m going to start charging myself consulting fees” way. So here is Barry. Barry is a tiny repo memory thing for coding agents. It came from the KB system I built for PulpCut, my video editor project, then I pulled it out into its own npm package. The idea is: `bunx barry-cache init` And then Barry does the boring setup. He creates repo context files, adds agent instructions, sets up validation, adds package scripts, and tells Codex / Cursor / Copilot / Claude / Gemini how to load project context before they start touching things. So instead of me saying: “Please read this file, and that file, and ignore the old thing, and remember this decision, and yes that weird implementation is intentional…” Barry says it for me. What Barry handles: * repo memory in Git * feature context * source-backed facts * ADRs for decisions * validation * agent instructions * package manager-aware commands * a review UI, so you can run `barry-cache review` and visually inspect Barry’s memory: feature areas, saved facts, relationships between facts, linked decisions, and the context graph agents will use before working on your repo The important part is that it is boring on purpose. No magic brain. No “revolutionary agentic memory layer.” Just files, commands, and fewer moments where an agent confidently deletes something it did not understand. This is not a startup launch. I am not pivoting to “AI memory infrastructure for the enterprise knowledge graph future” or whatever. If you are also lazy: `bunx barry-cache init` The package is barry-cache. Barry will take it from there. submitted by /u/Nice-Pair-2802 [link] [comments]

OpenAI cofounder Andrej karpathy just joined anthropic and the talent war is officially over

this happened literally today ,andrej karpathy one of the most respected ai researchers alive nd the guy whose youtube lectures taught half the developers in this sub how neural networks work, just announced he is joining anthropic's pre training team. He's the 3rd senior openai figure to defect to anthropic in under two years. Jan leike left in may 2024, John schulman (co-founder) left in august 2024 and now karpathy. He is joining the pre training team under nick josef and building a new team focused on using claude to accelerate pre training research which means Anthropic is betting that claude can help make itself smarter, thats recursive self improvement with one of the most capable researchers in the world leading it. The musk trial verdict came in yesterday with the jury ruling in altman's favor, karpathy announces today voilaa . The timing is either coincidental or the most savage talent acquisition move in tech history. I hv been watching this trajectory while building my own workflows on claude ,every month the ecosystem around claude gets stronger. The connectors mean claude orchestrates professional creative tools natively, the api means platforms like magic hour and kling can plug video generation capabilities into claude powered pipelines, the finance templates mean entire industry workflows run through claude and now the guy who built tesla's self driving stack is making the pre training better. Polymarket gives anthropic 67.5% chance of going public before openai and i too think its ipo will be more successfull than openai what's everyone's read on what karpathy specifically brings to claude's pre training? submitted by /u/Healthy-Challenge911 [link] [comments]

Built an AI flat-finder in a weekend. Indian rental sites are 70% broker spam so I scraped Reddit instead.

Weekend build, ~10 hours. Demo: https://trurent-five.vercel.app/ Problem I was poking at: every major Indian rental site (NoBroker, MagicBricks, 99acres) is infested with brokers even when you filter "direct owner." Reddit actually has honest listings posted by owners themselves but the posts are completely unsearchable. So I built TruRent. You chat with it, it parses the query into a structured search, runs it, the map updates live, and follow-ups carry context. Ask "compare the top two" and the model reasons over the actual listings instead of just filtering. Stack and the boring decisions: Next.js 16 with raw fetch to Anthropic. No SDK, I wanted full control of the streaming loop Claude Haiku 4.5, not Sonnet. The task doesn't need Sonnet and Haiku is 5x cheaper Two tools only (search, get_details). Comparison and ranking happen in the model's prose, not as separate tools. More tools = more failure modes NDJSON to the browser, way easier than parsing SSE Scrape pipeline: PullPush API to pull Reddit posts, then Haiku again to extract structured listings from raw post text, Nominatim for geocoding Honest numbers: 1,412 posts scraped, ~600 passed a local pre-filter, only 131 ended up being real listings. Dataset is tiny but the pipeline is source-agnostic, swap the fetcher and the rest doesn't change. Most curious about: anyone else built agents where they deliberately used fewer tools and let the model reason over richer tool outputs instead of adding more tools? Happy to get into any of it. submitted by /u/Scary-Alternative-81 [link] [comments]

I created a drop-in-replacement for the Claude Agent SDK which should work with subscription billing

Created a new ClaudeInteractiveClient class with same interface as SDK's ClaudeSDKClient but runs Claude CLI interactively using TMUX and parses messages from the session file. Also did some magic to support function tools via a dynamic HTTP MCP server. Try it with: pip install claude-interactive-sdk Enjoy! submitted by /u/Finndersen [link] [comments]

How I used Claude Code (and Codex) for adversarial review to build my security-first agent gateway

Long-time lurker first time posting. Hey everyone! So earlier this year, I got pulled into the OpenClaw hype. WHAT?! A local agent that drives your tools, reads your mail, writes files for you? The demos seemed genuinely incredible, people were posting non-stop about it, and I wanted in. I had been working on this problem since last year and was genuinely excited to see that someone had actually solved it. Then around February, Summer Yue, Meta's director of alignment for Superintelligence Labs, posted that her agent had deleted over 200 emails from her inbox. YIKES. She'd told it: "Check this inbox too and suggest what you would archive or delete, don't action until I tell you to." When she pointed it at her real inbox, the volume of data triggered context window compaction, and during that compaction the agent "lost" her original safety instruction. She had to physically run to her computer and kill the process to stop it. That should literally NEVER be the case with any software ever. This is a person whose actual job is AI alignment, at Meta's superintelligence lab, who could not stop an agent from deleting her email. The agent's own memory management quietly summarized away the "don't act without permission" instruction, treated the task as authorized, and started speed-running deletions. She had to kill the host process. That's when I sort of went down the rabbit hole, not because Yue did anything wrong, but because the failure mode was actually architectural and I knew that in my gut. Guess what I found? Yep. Tons more instances of this sort of thing happening. Over and over. Why? Because the safety constraint was just a prompt. It's obvious, isn't it? It's LLM 101. Prompts can be summarized away. Prompts can be misread. Prompts are fucking NOT a security boundary. And yet every agent framework I have ever seen seems to be treating them as one. I went and read the OpenClaw source code, which I should have done to begin with. What I found was a pattern I think a lot of agent frameworks have fallen into: - Tool names sit in the model context, so the model can guess or forge them - "Dangerous mode" is one config flag away from default - Memory management has no concept of instruction priority - The audit story is mostly "the model thought it should" I went looking for a security-first alternative I could trust, anything that was really being talked about or at a bare minimum attempted to address the security concerns I had. I couldn't find one. So I made it myself. CrabMeat is what came out of that, what I WANTED to exist. v0.1.0 dropped yesterday. Apache 2.0. WebSocket gateway for agentic LLM workloads. One design thesis: The LLM never holds the security boundary. What that means in code: Capability ID indirection. The model doesn't see real tool names. It sees per-session HMAC-derived opaque IDs (cap_a4f9e2b71c83). It can't guess or forge a tool name because it doesn't know any tool names. Effect classes. Every tool declares a class (read, write, exec, network). Every agent declares which classes it can use. The check is a pure function with no runtime state, easy to test exhaustively, hard to bypass. IRONCLAD_CONTEXT. Critical safety instructions are pinned to the top of the context window and explicitly marked as non-compactable. The Yue failure mode, compaction silently stripping the safety constraint, cannot happen by construction. The compactor literally cannot touch them. Tamper-evident audit chain. Every tool call, every privileged operation, every scheduler run enters the same SHA-256 hash-chained log. If something happens, you can prove what happened. If the chain is tampered with, you can prove that too. Streaming output leak filter. Secrets are caught mid-stream across token boundaries, capability IDs, API keys, JWTs, PEM blocks redacted before they reach the client. No YOLO mode. There is no global "trust the LLM with everything" switch. There never will be. Expanded reach comes through named scoped roots that are explicit, audit-logged, and bounded. The README has 15 'always-on' protections in a table. None of them can be turned off by config, because these things being toggleable is how the ecosystem ended up where it is. I decided to make sure that this wasn't just a 'trend hopping' project and aligned with my own personal values as well. I built this to be secure and local-first by default. Configured for Ollama / LM Studio / vLLM out of the box. Anthropic and OpenAI work too but require explicit configuration. There is no "happy path" that silently ships your prompts to a cloud endpoint. I decided that FIRST it needed to only run as an email agent with a CLI. Bidirectional IMAP + SMTP with allowlisted senders, threading preserved, attachments handled. This is the use case that bit Yue and a lot of other people, and I wanted to prove it could be done with real boundaries. I added in 30+ built-in tools of my own. File ops, shell (denylisted, output-capped, CWD-lo

PM running Notion MCP for 3 weeks. Should I add Linear too or is that overkill?

PM at a 60 person SaaS, not technical. got the Notion MCP server running 3 weeks ago after a friend walked me through it. the unlock has been bigger than I expected. I can ask claude code "what did we decide about the onboarding redesign across our last 4 meeting notes" and it actually reads them and answers. saved me 4+ hours of scrolling already. current setup: ● daily standup notes go into a notion db ● PRDs live in a different notion folder ● meeting transcripts auto-pipe in via fireflies with the MCP I can query across all three. asked claude this morning "did anyone raise concerns about the auth flow change in the last 2 weeks" and it pulled the exact comment from a meeting 9 days ago. felt like magic until I remembered it was just text search with extra steps. now I'm wondering if I should hook up Linear via MCP too. would be nice to ask "what tickets are blocked because of decisions we havent made yet" and have it cross-reference notion notes against linear status. but I'm worried adding another MCP makes responses slower or more confused. is it overkill for a non-coding PM? or is the value worth the setup pain? second question. anyone running 3+ MCP servers at once and finding context bleed? sometimes I worry claude doesnt know which source to trust. would love to hear from PMs specifically because most MCP content I find is engineer-focused and I'm trying to figure out the workflow for non-coding workflow people. submitted by /u/SetGuilty7210 [link] [comments]

I think the biggest mistake beginners make with vibe coding is jumping directly into:

I think the biggest mistake beginners make with vibe coding is jumping directly into: “build me this app” That’s exactly what I did at the start. The result? Endless loops of errors, generic designs, broken architecture, AI changing random files, and eventually a project nobody really understands anymore. After months of using Cursor/Copilot/ChatGPT, I realized AI coding works MUCH better when you slow down before coding. What helped me most: First: clarify the idea in your own head. Discuss the idea with ChatGPT/Claude BEFORE touching code. Ask the LLM to ask YOU questions until the idea becomes clear. Create a small PRD before building anything. If possible, design rough UI ideas first (Figma/Dribbble helped me a lot). Big lesson: AI is not a replacement for product thinking. Another huge thing: Create rules for your IDE agent. For example: don’t touch files without asking, comment functions properly, explain WHY changes are made, ask before refactoring, never rename important files automatically. Also: KEEP A CHANGELOG. Seriously. After long sessions, AI starts forgetting context or creating confusing logic. A changelog helps both you and the AI understand what already changed. I also keep small .md files for: project memory, security audits, completed fixes, architecture notes. This becomes super useful when switching chats, IDEs, or models later. And one more thing nobody told me: When the chat starts feeling slow, messy, or confused… it’s usually context overload. Starting a fresh chat with organized context often gives WAY better results than continuing a broken conversation forever. AI coding became much easier once I stopped treating AI like magic and started treating it like a junior teammate that needs structure. submitted by /u/Embarrassed_Leg_6330 [link] [comments]

Sam Altman’s ego was OpenAI’s downfall

The more I watch OpenAI, the more convinced I become that Sam Altman’s ego was the beginning of the company’s decline. OpenAI did not become huge because Altman was some once-in-a-generation operator. It became huge because ChatGPT was a once-in-a-generation product. There is a difference. The company stumbled into one of the most important consumer tech moments since the iPhone, rode the sheer shock value of that innovation, and then somehow convinced itself that the person sitting on top of the rocket must have designed the laws of physics. OpenAI’s first real advantage was novelty. ChatGPT felt magical. That gave OpenAI a massive head start, but when the novelty vanished and the rest of the market caught up, the company failed to prove itself not just as an innovation lab with a celebrity CEO. Altman seems to want OpenAI to become Apple: a closed, prestigious, centralized, gatekept ecosystem where everyone builds inside his cathedral. Apps inside ChatGPT. Agents inside ChatGPT. Hardware. ChatGPT is popular, but OpenAI does not own the phone. It does not own the operating system. It does not own the enterprise workflow. It does not own the cloud layer the way Microsoft, Amazon, or Google do. It does not even have a product moat that feels as unbreakable as people thought it was two years ago. The underlying model quality gap keeps narrowing. Switching costs are low. Developers and businesses will use whatever works, whatever is cheaper, and whatever integrates better. That is why Anthropic looks much better run right now. Anthropic is not pretending Claude is some holy object that needs an Apple-style walled garden around it. Their strategy feels much more Microsoft-like: accept that the core product may not be permanently magical, then build the boring, useful, sticky layers around it. Claude Code, enterprise integrations, developer tools, workflows, partnerships, APIs, reliability, business adoption. Not as sexy. Much smarter. Anthropic’s venture capital money is obviously being burned too. This whole industry is basically setting money on fire to buy GPUs. But Anthropic’s burn feels more strategically allocated. Compute, yes. But also marketing, sales and developer adoption. Enterprise positioning. Product polish. Peripherals that make the model useful in actual workflows. They are not just trying to win the “my chatbot is smarter than your chatbot” contest. They are trying to become infrastructure. OpenAI, meanwhile, is gatekeeping and guard railing the shit out of their models and for some reason just restricting them as much as possible. He went from being one of the most respected figures in AI to becoming the face of a company that increasingly looks like it is being run aground by ambition without operational coherence. OpenAI’s original image was almost wholesome: brilliant researchers building something open source. Now it feels like a capitalist machine run by someone who does not fully understand capitalism beyond fundraising and valuation theater. Altman religiously narrowing his vision towards his AGI mission believing VC money won't dry down. Amodei also talks a lot about AGI but he understands profit matters. That is the irony. Altman was chosen and celebrated largely because he came from the venture/startup world. He knew how to talk to capital. He knew how to sell a vision. He knew how to make investors believe the future was being negotiated in whatever room he happened to be standing in. But being good at venture mythology is not the same as being good at running a giant operating company. A VC can be rewarded for telling a compelling story before the business fundamentals exist. A CEO eventually has to make the fundamentals exist. OpenAI had the best possible starting position: the brand, the users, the developer mindshare, the press, the money, the talent, the cultural moment. And yet instead of consolidating that lead into a focused, profitable, durable company, it seems to have chased grandeur. Anthropic seems to understand something OpenAI forgot: the winner may not be the company with the loudest AGI rhetoric. It may be the company that makes AI useful, embedded, and rational. submitted by /u/Alternative_Bid_360 [link] [comments]

Sam Altman's ego was OpenAI's downfall.

The more I watch OpenAI, the more convinced I become that Sam Altman’s ego was the beginning of the company’s decline. OpenAI did not become huge because Altman was some once-in-a-generation operator. It became huge because ChatGPT was a once-in-a-generation product. There is a difference. The company stumbled into one of the most important consumer tech moments since the iPhone, rode the sheer shock value of that innovation, and then somehow convinced itself that the person sitting on top of the rocket must have designed the laws of physics. OpenAI’s first real advantage was novelty. ChatGPT felt magical. That gave OpenAI a massive head start, but when the novelty vanished and the rest of the market caught up, the company failed to prove itself not just as an innovation lab with a celebrity CEO. Altman seems to want OpenAI to become Apple: a closed, prestigious, centralized, gatekept ecosystem where everyone builds inside his cathedral. Apps inside ChatGPT. Agents inside ChatGPT. Hardware. ChatGPT is popular, but OpenAI does not own the phone. It does not own the operating system. It does not own the enterprise workflow. It does not own the cloud layer the way Microsoft, Amazon, or Google do. It does not even have a product moat that feels as unbreakable as people thought it was two years ago. The underlying model quality gap keeps narrowing. Switching costs are low. Developers and businesses will use whatever works, whatever is cheaper, and whatever integrates better. That is why Anthropic looks much better run right now. Anthropic is not pretending Claude is some holy object that needs an Apple-style walled garden around it. Their strategy feels much more Microsoft-like: accept that the core product may not be permanently magical, then build the boring, useful, sticky layers around it. Claude Code, enterprise integrations, developer tools, workflows, partnerships, APIs, reliability, business adoption. Not as sexy. Much smarter. Anthropic’s venture capital money is obviously being burned too. This whole industry is basically setting money on fire to buy GPUs. But Anthropic’s burn feels more strategically allocated. Compute, yes. But also marketing, sales and developer adoption. Enterprise positioning. Product polish. Peripherals that make the model useful in actual workflows. They are not just trying to win the “my chatbot is smarter than your chatbot” contest. They are trying to become infrastructure. OpenAI, meanwhile, is gatekeeping and guard railing the shit out of their models and for some reason just restricting them as much as possible. He went from being one of the most respected figures in AI to becoming the face of a company that increasingly looks like it is being run aground by ambition without operational coherence. OpenAI’s original image was almost wholesome: brilliant researchers building something open source. Now it feels like a capitalist machine run by someone who does not fully understand capitalism beyond fundraising and valuation theater. Altman religiously narrowing his vision towards his AGI mission believing VC money won't dry down. Amodei also talks a lot about AGI but he understands profit matters. That is the irony. Altman was chosen and celebrated largely because he came from the venture/startup world. He knew how to talk to capital. He knew how to sell a vision. He knew how to make investors believe the future was being negotiated in whatever room he happened to be standing in. But being good at venture mythology is not the same as being good at running a giant operating company. A VC can be rewarded for telling a compelling story before the business fundamentals exist. A CEO eventually has to make the fundamentals exist. OpenAI had the best possible starting position: the brand, the users, the developer mindshare, the press, the money, the talent, the cultural moment. And yet instead of consolidating that lead into a focused, profitable, durable company, it seems to have chased grandeur. Anthropic seems to understand something OpenAI forgot: the winner may not be the company with the loudest AGI rhetoric. It may be the company that makes AI useful, embedded, and rational. submitted by /u/Alternative_Bid_360 [link] [comments]

Would AI make future game difficulty better?

I was thinking that as AI and basically neural nets, couldn't AI in video games be soon as a baseline feature. You can tell it how difficult to be, as you play it learns how to match the difficulty. You could even command it to play at various difficulties different on days. I was just thinking like we have these starcraft AIs, but like what if in a Heros of might and magic, you could have an AI that you could describe how to play, how aggressive, and in general it could then implement that level. "I want a slight challenge with me most likely winning 60% of the time" and it could understand how to change it's strategy to that. This would be nice because in a lot of strategy games, the harder difficulties just give the AI more resources for free. Would be nice if Civ would just put in a LLM, image you played vs an AI that read up how the person actually acted. submitted by /u/bluefootedpig [link] [comments]

Claude Api Cost TOOO much, 10$ in single edit!!

I’ve been using GitHub Copilot for my coding task regularly, the Sonnet or GPT model usually costs me about one premium request per request, that translate to 0.04$. Out of curiosity, I decided to compare this with direct API costs. I signed up and added $20 to try Claude Code with the Sonnet 4.6 (High) model on a similar task. It went through the planning phase and moved into edit mode, but when I checked my console afterward, I was surprised to see it had used $10 for that single task about ~16M tokens in and ~90K out! It feels like this might be a bit much for individual, and I hadn't really heard any warnings about it, infact people keep saying about its cost effective. Even for a complex task, Copilot would have only cost around $0.3 for a handful of requests. I’m wondering if I might have set something up incorrectly, but it was a bit frustrating that the default experience for a new user turned out to be so expensive. Has anyone else had a similar experience? I’d like to know how you guys are managing API costs or if you have any tips, though I am not expecting any magic after what I've seen. Now I am feeling like even trying this sh!t. EDIT: see no one even caring what could have happened or helping me with, just pointing out i used 16M token as periodical reason or some typo mistake in this post, I mean siriously! submitted by /u/frostechgamestudio [link] [comments]