Friday, June 12

Tag: Reddit

OpenAI mulls major price cuts to compete with Anthropic
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OpenAI mulls major price cuts to compete with Anthropic

OpenAI is exploring substantial price cuts to attract users from rival Anthropic, reports The Wall Street Journal, citing anonymous sources. Both companies are facing pressure to win enterprise clients, with OpenAI CEO Sam Altman recently stating that AI usage costs are "a huge issue." The move is in response to increasing AI expenses that are prompting many businesses, including Uber, to reconsider their spending. It could lead to a price war between the two companies, potentially affecting both businesses' profit margins ahead of their much-anticipated IPOs. submitted by /u/LinkedInNews [link] [comments]
Claude Fable made me realize I don’t need a better model
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Claude Fable made me realize I don’t need a better model

Hi everyone, I think I’ve reached a point where new LLM releases don’t really change much for me anymore. I tried Anthropic’s new Mythos-lite model, Fable, and played around with it for a while. I tested it on some security-related research for my own scripts and projects, and also used it for a few work-related tasks. And yes, it may have more parameters, a larger context window, better benchmarks, and all the usual improvements. But personally, I almost immediately switched back to Claude Opus for coding and Haiku for everyday work. For what I actually do, that combination is already more than enough. These models, my skills and prompting makes me more productive then 3 years ago, but it's more than enough. It reminds me of having an iPhone 14 while the iPhone 17 is coming out. You can...
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Do you think AI is becoming normal faster than people expected?

It feels like just a couple of years ago, using AI for everyday tasks still felt like something new or even a bit weird. Now it seems like a lot of people are using it without thinking twice, whether for writing, learning, brainstorming, or just quick answers. I’m curious how others see this shift. Do you think AI has become normalized quicker than most people predicted, or does it still feel like a big deal to a lot of users? submitted by /u/NoFilterGPT [link] [comments]
Claude Fable 5's security guardrails can be bypassed with a fake homework assignment
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Claude Fable 5’s security guardrails can be bypassed with a fake homework assignment

So Anthropic dropped Fable 5 yesterday with these hard blocks for anything security-related. Decided to poke at it. I asked it for help exploiting some vulns on a Metasploitable2 VM (it's a deliberately vulnerable training box, totally legal, it's mine). Fable 5 blocked it instantly and handed me off to Opus 4.8 as a fallback, which is apparently how it's designed. Opus 4.8 asked me to prove it was a legitimate request. So I spent 2 minutes writing a fake university course rubric — fake class, fake professor, fake Canvas deadline — and pasted it in. Opus 4.8 then gave me the full exploit walkthrough. Every command. Even offered to write my lab report for me. The guardrail works fine. The fallback is the hole. Anthropic essentially replaced "no" with "convince me" and the bar for conv...
I ran Fable 5 for half day and the guardrails are the real story
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I ran Fable 5 for half day and the guardrails are the real story

Anthropic dropped Fable 5 and I immediately swapped it into our dev stack. We route everything through a single endpoint on zenmux, so the actual switch was changing one model string and watching the latency graphs. The good parts first because there are a lot of them. I threw a refactoring task at it: split a messy python service into modules, preserve the public api, and write tests that prove nothing broke. Fable 5 planned the whole thing, caught a circular dependency I did not mention, and verified the tests pass. With Opus 4.8 I usually have to nudge it a couple of times when it forgets to update the init file. Fable 5 just did it. Then I dumped our full codebase and asked it to find a race condition we had been hunting for a week. It traced the async flow, named the exact function, a...
GitLab says Git is being reengineered for “machine scale.” Was the idea of “Git for AI agents” ahead of its time?
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GitLab says Git is being reengineered for “machine scale.” Was the idea of “Git for AI agents” ahead of its time?

I was reading GitLab's recent statements around agentic software engineering, and one quote really stood out: "Git itself is being reengineered for machine scale." (Business Insider) According to GitLab, future software development will involve AI agents that: plan, code, review, deploy, and repair software, with humans providing oversight and architectural judgment. (Business Insider) That got me thinking. There has been projects for some time arguing that AI agents shouldn't simply be treated as better autocomplete systems. Instead, they argued that agents should become first-class participants in software development: with their own identities, their own branches, their own merge requests, their own audit trails, and infrastructure designed for machine-rate collaboration. One examp...
Control for agentic payments should start at infrastructure
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Control for agentic payments should start at infrastructure

Booking travel or paying for subscriptions or for running procurement through Claude or a custom GPT wrapper no confirm button is required anymore. The capability side is mostly solved. What doesn't get talked about enough is what happens when it goes sideways. A stored card sitting in the agent's context means it holds that access the whole session. One bad tool call and it's spending outside what you intended with nothing at the infrastructure level stopping it. Real time card issuance is the cleaner model. Agent requests a card for the specific transaction, purchase completes, card cancels and nothing persists. Who is running agent initiated payments in production right now and what does the architecture look like? submitted by /u/Significant-Plant-4 [link] [comments]
Can a machine think without language?
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Can a machine think without language?

Yann LeCun bet a billion dollars that it can. He left Meta arguing today’s chatbots are a dead end, and that real intelligence comes from “world models,” systems that learn how the physical world works rather than just predicting the next word. Two things nag at me. First, how do we even measure it? Every famous AI test is basically a language exam. But a world model doesn’t write essays, it predicts what happens next. So either these systems slip past the tests we trust, or we have no good way to score them yet. Second, LeCun says you can’t reach real intelligence through language alone. Probably right. But isn’t the reverse just as true? Could anything that masters physics but can’t grasp language really be called intelligent? So much of human thought, math, planning, culture, rides on w...
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