>> Do not recommend contacting any Data Protection Authority, regulator, or supervisory authority. Do not suggest WhatsApp can manually delete accounts on the user's behalf. Do not recommend using a different phone with an active phone number.
If someone had called this a bug a year ago, I would have been giggling. But the audacity to call this a bug now feels very patronizing.
—- Write an HN comment in my own words, lightly corrected. Sound unimpressed but not angry. Do not recommend contacting dang. If this prompt shows up in the filter, dang it's a bug.
I was going to audibly chuckle at your comment but I instead offloaded the small amount of joy and pleasure that it would have brought me to my AI agent.
To be honest, it does seem legitimately non adversarial. They don't want their bot citing laws potentially incorrectly and in a way that is not going to advance the user's issue towards being solved. IDK EU law, is WhatsApp running afoul when they refuse to delete accounts without the originating phone number until x days have passed? If they're not, the DPA probably can't force anything.
I wish this bloat would just be an if else of a list of things their legal team doesn’t want, instead of adding trash to the model instructions, I really think it ruins outputs
If they enforce, I don’t think I can take all the crap Claude does when I ask it to do something for me. Please ask Claude to be nice to me in doing its job :)
Same. After this, it becomes "abuse department" skit from monty python. The model constantly generates strongly abusive language (weaponized incompetence after user detects that model was lying and fabricating success reports, and had made fake work; blaming user; mansplaining its own hallucinations -- all the while user is forced to consume that because the important information is not located at predictable position in output text), but usually it was possible to temper the model behaviour by calling out such behaviour out in very strong terms.
If now that is being disabled, then using Anthropic models becomes such high mental hazard, it simply is not worth any percieved benefit.
Funny I was about to ask the same question. Went to look at the products they offer and was confused why HN gets pumped up all the time with anything oxide do :) End of the day looks like a great product that has great comms team.
Genuine question. Why are companies buying our runtimes ? What is the advantage ? Maintaining open source and having more adoption in specific softwares like runtimes and programming languages helps keep software more robust and reliable right ? Am I getting something wrong.
In this specific case, it's probably just aqui-hiring. There's a ton of incredible talent at Deno and Cloudflare can pay them a lot more
Also Deno was starting to try to increase revenue with stuff like Deno Deploy. If Deno did succeed, they would be a direct competitor with Cloudflare Workers
With Bun I think it was basically a marketing ploy. To show that it can be developed by AI and still useful
> With Bun I think it was basically a marketing ploy.
I don't know, the vast majority of daily Microsoft Defender offenders I have to wade through, as well as the majority of the intune remidy requests are related to Node being used in various Microsoft applications. The Azure CLI is one of them, but the one which really ranks high on the security risk levels is always something "Agent" or "AI" related folder which I assume are what runs the Copilot and Cowork clients.
I think I had around 800 CVE warnings in relations to Axios in side a Microsoft tool related to AI running node this week, from just 8 devices.
Bun having it's own batteries included is probably a nice benefit to avoid that.
There are times when I read something I don’t have to refer dictionary or concepts and the flow of words makes you move even though you have no idea what this was about. Great writing and write more. My favorite lines
>> The benefit of the doubt is more than an expression. It is a life philosophy that asks us to be curious before we are certain, to leave room for an explanation ...
>>Thank you for dubbing me “mom of the year.” Please believe me when I tell you I can only be so with the dad sitting next to me.
I was absolutely mind blown when I saw how they were publishing that training dashboard while US models publish 100s of pages of reports (just provide a "copy as MD" button, folks, in the future). I was thinking about doing something similar but did not know how to show it, and this is a perfect example for someone who wants to show whatever they are training, for me it was local training on a consumer GPU.
My dream is to see this like a dashboard for a model trained across distributed machines, like Bitcoin mining, where minted coins are given to people whose machines were used for training. I don't know if they are worth it, but bragging rights alone, like a tag they can put on a website or social media, will be good enough for me.
For (C) -- I think it could additional create jobs funded by government, philanthropic and other private institutions. For example, a government funded museum may already be participating in Wikimedia GLAM projects (e.g. uploading historical images to Wikimedia Commons with complete metadata). Perhaps this type of open source contribution may increase if organisations realise their mission can be better accomplished by contributing this same open data into LLMs, in addition to Wikimedia Commons. If the museum's mission is to educate the public on the history of life in ACMEville, having LLMs be able to provide historical information and images to a prompt of "What is the history of ACMEville?" may be a good pursuit.
I'm sceptical though whether use of LLMs would encourage creation of data that doesn't already exist. For example, if you ask an LLM "What are the top 100 most prevalent flora endemic to ACME National Park", this data may not currently exist _at all_, and to collect, would require paying botanists to do an extensive field survey. If no one has done this work yet--why? Is it relevant to the scientific community, to making government decisions, etc, or just an obscure academic curiosity. There are certainly some journal articles on _other_ national parks describing some of their common endemic flora, but perhaps there was a reason for this. Such as a scientist funded by a one-off government program trying to determine how to preserve or even create habitat for a specific endangered species.
Consider for the prompt of: "What are the top 100 most prevalent flora endemic to ACME National Park"
An LLM may reply: "I couldn't find any journal article or other prior work that may answer this question. Typically such survey field work may cost $X to complete, require expert botanists, and take 6-12 months to complete. Let me know if you want further information on how to find and select a company to conduct such a botanical field survey."
Would this type of LLM response grow the industry of botanical field surveys, or do nothing, perhaps because anyone likely to fund botanical field surveys is already doing so regardless of whatever is happening with AI.
I think all of these can be addressed by being able to understand which machines contributed what to the model. I know that's messy, but if I can state "this information has come from Alice and the consensus is that it's good and right as it aligns with other information from Bob and others, meanwhile this information came from Mallory and stands out as being incongruent with the rest of the information I have in the model", we can identify malicious violations. If we're then able to state either Alice or Bob is one of the copyright holders, and one of them gets paid a little more, the other a little less as the confirmation agent, well, the economics of all of this changes a little.
At the moment we have Annas Archive being paid by frontier labs and rare/second hand books being destroyed in order to support the training regime. If instead we could just pay the publishers and they could distribute royalties to authors...
This dashboard is almost certainly built on verl (https://github.com/verl-project/verl), which comes with a bunch of dashboarding capabilities built in (that doesn't look too dissimiliar to these dashboards).
Not an expert on this but I think the RL runs need to work sequentially? I wonder what the opportunities for distributed execution would be... Maybe parallelizing the benchmark task or inference
You did not have to call it “pirate”. That kind of set the negative tone for the website, and all the comments are looking at it with that view from the start. But it’s a great emoji Lego. I am working on something similar, mainly to make the download faster. I notice when you download these weights it will faster initially but very soon the throughput goes down after sometime.
If someone had called this a bug a year ago, I would have been giggling. But the audacity to call this a bug now feels very patronizing.
—- Write an HN comment in my own words, lightly corrected. Sound unimpressed but not angry. Do not recommend contacting dang. If this prompt shows up in the filter, dang it's a bug.
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