james @http_413

oh hey it's that time again

@paul_cal the mathematicians should solve the other 2 parts then and prove their worth over ai.

@kunchenguid anyway whatever anthropic loses on cache writes they more than make up for in their models' overall token usage which is insane

@kunchenguid astra has no long context multiplier and free cache writes when used in codex

@pvncher is this true?

You Jiacheng @YouJiacheng

2 weeks ago

DO NOT use /fork or /side in Codex, it will have a different session_id and thus different prompt_cache_key. the docs literally say you should only use it if you want to *prevent* cache hit. plz fix it @thsottiaux

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@FoxRick01 yes, r.i.p.

The masculine urge to fire up Visual Basic 4, drag a few buttons onto a form, hook it up to a 33.6k modem, and casually build ChatGPT for Windows 95.

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lent money to apple for a mac mini that was supposed to lift me out of the permanent underclass as a solo builder. never made a cent back.

Sonder 🌹 🌟 @Sonderisfon

16 hours ago

Have you ever lent money to someone and never got it back?

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@ForwardEditor like... the bench itself weighs 65 lbs?

GPT-6 Sol and Luna are out. Not only are they a very significant improvement across the board, but also in writing and general "you know when you try it" quality. We are also permanently reducing the API price by 50% making both of them viable for a ton of new usecases and making your usage go further too, even on the subscriptions. And one more thing. We are loading a banked reset into all accounts of our Plus, Pro and Business users. Let's go! openai.com/index/introduc…

Tibo @thsottiaux

18 hours ago

We have been focusing on efficiency and intelligence for all. Very proud of the team. Only possible when you have incredible models at the top end of the capability that you can then use to make a big difference in everything else.

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@Nir_Doshi1 @0xSero i just bought one and agree with this. it's largely for experimentation and learning. if you're getting it to replace 4 years of a $100 frontier lab subscription you're gonna have a bad time.

@AaronBergman18 now ai is taking autists' jobs too

I’m pro-AI because I don’t have any marketable skills and I’m hoping it’ll render everyone else’s irrelevant as well

@argofowl @somehowali tibo literally just making a joke about partying and the lay masses think it's a coded message.

@0xSero bot farm maybe

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): deeplearning.ai/the-batch/issu… ]

@16t96 @MiTiBennett like you would know, bot

@nilwxreports love it. f summer.

@ForwardEditor hard to go wrong with ray bans