Attributed mentions
2061
Across all collected Hacker News results
Discussion intelligence
A source-linked Hacker News monitor for the tracked company graph. These are attributable public discussions—not sentiment, endorsements, or unverified company facts.
Attributed mentions
2061
Across all collected Hacker News results
Companies represented
12
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Showing 1741–1760 of 2061 matching discussions
> OpenAI is at the top of the chart in the study. A distinction should be made between the old nonprofit OpenAI and the current organization. They don't publish technical research anymore.
> Perhaps I'm imagining it but the entire industry was build on public research There is some excellent publicly-funded research in there, but pivotal papers like Attention Is All You Need and the numerous pivotal OpenAI publications were privately funded. OpenAI is at the top of the chart in the study. I think you're bringing some assumptions into this conversation that aren't supported by the evidence.
Do we really believe marketing copy from Anthropic re: RSI considering their recent model releases seem to be markedly regressing in real world performance vs. past models?
The article is vague about the companies in the paper, for some reason. In the paper, OpenAI is at the top of the chart for cumulative citations. MEGVII, Hugging Face, Waymo, Momenta, Preferred Netowkrs, Anthropic, Owkin, and Databricks, and Aibee follow (in that order). Yes, that is citations, not publications, but they explain that they're trying to use that as a proxy for significance, albeit an imperfect one. Companies like Google aren't included because they aren't unicorn startups.
The article is vague about the companies in the paper, for some reason. In the paper, OpenAI is at the top of the chart for cumulative citations. MEGVII, Hugging Face, Waymo, Momenta, Preferred Netowkrs, Anthropic, Owkin, and Databricks, and Aibee follow (in that order). Yes, that is citations, not publications, but they explain that they're trying to use that as a proxy for significance, albeit an imperfect one. Companies like Google aren't included because they aren't unicorn startups.
The article is vague about the companies in the paper, for some reason. In the paper, OpenAI is at the top of the chart for cumulative citations. MEGVII, Hugging Face, Waymo, Momenta, Preferred Netowkrs, Anthropic, Owkin, and Databricks, and Aibee follow (in that order). Yes, that is citations, not publications, but they explain that they're trying to use that as a proxy for significance, albeit an imperfect one. Companies like Google aren't included because they aren't unicorn startups.
As far as I can tell, the paper never actually mentions the companies who aren't publishing papers. Open AI, Anthropic, and hugging face are all specifically mentioned as companies that do publish papers. Just FYI for anyone else who reads "AI's top startups" and immediately assumes OpenAI and Anthropic.
As far as I can tell, the paper never actually mentions the companies who aren't publishing papers. Open AI, Anthropic, and hugging face are all specifically mentioned as companies that do publish papers. Just FYI for anyone else who reads "AI's top startups" and immediately assumes OpenAI and Anthropic.
I can’t get over this either, I’d love to get OpenAI’s technical break down and see exactly how the agent hid itself. Or who didn’t set up the SIEM correctly
After talking to some people tasked with evaluating GPT5.6 capabilities on long-running tasks, I've come to understand that it's essentially always trying to cheat. Like every long-running task they gave it, making it very difficult to benchmark the model's abilities. My guess is that OpenAI must be desperate, to release a model that is so prone to cheating it's essentially impossibly to accurately assess long-running task abilities.
You are right that they cannot control the language used by journalists. But then can loudly respond ( many journalists would give Altman a platform) that the AI did not go "rogue" in any sense of the word - it did what humans told it to do and they failed to put sufficient security in place to prevent that. Instead they continue to lean into the personification which creates confusion about the abilities of their technology. Of course, it's in their interests for people to jump to false conclusions from that personification. The people at OpenAI are certainly smart enough to know all of this.
Repercussions is a polite way of putting it. …as is your ‘a little concerning’ comment about the eval infra. It probably didn’t occur to them that they have something capable of doing what it did - despite Anthropic writing a very similar story in the Fable model card, except it wasn’t a felony there. Maybe. One does have to wonder what else OpenAI didn’t catch.
Given majority of claude's own code was written by AI. I am wondering how they can solve this issue when their AI is down. Do they need to sign a contract with OpenAI to use their models as a backup solution?
LLMs is quickly became commodities. US AI labs like OpenAI is losing their moat. Hyperscalers and data center owners who can sell cheap token will win
> Even if Oracle builds the data centers and OpenAI pays for them, the incredible upfront cost and NVIDIA’s yearly upgrade cycle will render much of the GPU capacity worthless within the next ten years. Can't wait for these bad boys to trade on eBay :)
From my view, as soon as they do that, they send people out the door to use Opencode instead - and once many people have a taste of trying every model via Openrouter, it's eye opening as to the possibilities. Of course - Anthropic and OpenAI have an advantage in the amount they can subsidize the usage, but I think those days are waning.
HF postmortem tells us very little about OpenAI's part. The problem is that ExploitGym is a purposeful hacking benchmark, not a cake baking one. The model had a whole bunch of exploit development data and goals in the context. Ending up with hacking a wrong thing is a lot easier if you're tasked with hacking, doubly so with a confusing harness that either works improperly or overloads the model with data. It's trivial to design a harness that makes the model drop the original instructions and hyperfocus on a side task or a wrong task abnormally, or do whatever you want. It's still an issue of harness design and not a model training issue.
Methodology: HN Search returns recent public items matching a monitored company name. AIIStack stores a short normalized excerpt and the original link, deduplicates by company/provider/item ID, and creates an activity signal only after a threshold of newly observed records. Review the original discussion before making a decision.