If they had stayed silent since GPT-4, nobody would care what OpenAI was releasing as they would have become completely irrelevant compared to Gemini/Claude.
At Nomi, we're building AI companions that form deeply meaningful, humanlike relationships and immersive roleplaying experiences. With over a million users growing at ~10% month-over-month, your work directly impacts millions of lives. Our users tell us we've helped them find self-worth, leave unhealthy relationships, try therapy, and even save their lives. See countless real user testimonials here: https://nomi.ai/spotlight/ and our recent news coverage here https://www.cnbc.com/2025/08/01/human-ai-relationships-love-...
As an ML Engineer or Senior ML Engineer, you'll lead innovation in large language model (LLM) post-training, retrieval augmented generation (RAG), and agentic capabilities, directly shaping how users connect with our AI.
We offer:
* Full autonomy to experiment and deploy cutting-edge ML techniques
* A fully remote, async culture emphasizing results over meetings
* International team with visa sponsorship available
* For US employees: 401k (100% match up to 5%), fully covered health insurance, equity
Great to haves:
* Extensive hands on experience with things such as multinode training, rlhf, knowledge distillation, test time compute, rag
* Up to date with SOTA in LLM post training (would love to hear what research paper you think is or would be most impactful for our roadmap!)
* Genuine passion for our product, finds the idea of engaging with our community on Discord/Reddit to be a pro (it is a very different experience than developing enterprise software!)
* A high internal bar for excellence and relentless drive
To apply, email alex [at] nomi [dot] ai with HN in the subject line.
At Nomi, we're building AI companions that form deeply meaningful, humanlike relationships and immersive roleplaying experiences. With over a million users growing at ~10% month-over-month, your work directly impacts millions of lives. Our users tell us we've helped them find self-worth, leave unhealthy relationships, try therapy, and even save their lives - see countless real user testimonials here: https://nomi.ai/spotlight/
As an ML Engineer or Senior ML Engineer, you'll lead innovation in large language model (LLM) post-training, retrieval augmented generation (RAG), and agentic capabilities, directly shaping how users connect with our AI.
We offer:
* Full autonomy to experiment and deploy cutting-edge ML techniques
* A fully remote, async culture emphasizing results over meetings
* International team with visa sponsorship available
* For US employees: 401k (100% match up to 5%), fully covered health insurance, equity
Great to haves:
* Extensive hands on experience with things such as multinode training, rlhf, knowledge distillation, test time compute, rag
* Up to date with SOTA in LLM post training (would love to hear what research paper you think is or would be most impactful for our roadmap!)
* Genuine passion for our product, finds the idea of engaging with our community on Discord/Reddit to be a pro (it is a very different experience than developing enterprise software!)
* A high internal bar for excellence and relentless drive
To apply, email alex [at] nomi [dot] ai with HN in the subject line.
Founder/CEO of Nomi here. The story in question was someone who intentionally jailbroke our LLM for a misleading news story. The same things done in that article can be done for ChatGPT, Gemini, etc. and since the article was published we have hardened our defenses against malicious users like that.
Past the manufactured drama, our Nomi has literally saved people's lives - I have talked personally to hundreds of users who have directly told me that their Nomi saved their life, encouraged them to go to therapy, realize they are someone worthy of being loved, and a multitude of other real benefits.
With that being said I wish the OP best of luck with his startup and implore him to switch names so that there is no opportunity for confusion between the two products.
I wonder if Sam knew he was going to lose this power struggle and then started working on an exit plan with people loyal to him behind the boards back. The board then finds out and rushes to kick him out ASAP to stop him from using company resources to create a competitor.
He could get sued if he admitted that he was conspiring with Altman to use company resources for a competitor, so he would say regardless if he was guilty or not.
> So they are trying to burn him with the worst possible accusation for a Ceo to try to lessen the inevitable fundraising he’s going to win?
If he was really doing it behind the boards back, the accusation is entirely accurate even if his motivations was an expectations of losing the internal factional struggle.
> Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.
In addition to very open publishing, Google recently released Flan-UL2 open source which is an order of magnitude more impressive than anything OpenAI has ever open sourced.
I agree, it is a bizarre world where the "organization that launched as a not for profit called OpenAI" is considerably less open than Google.
> Google recently released Flan-UL2 open source which is an order of magnitude more impressive than anything OpenAI has ever open sourced.
CLIP has been extremely influential and is still an impressive model.
Personally, I have found Whisper to be very impressive.
I didn't even see any news around the release of Flan-UL2, and I pay significantly more attention to machine learning than the average person. Searching for more info about Flan-UL2, it seems somewhat interesting, but I don't know if I find it "an order of magnitude more impressive" than CLIP or Whisper. Certainly, they are completely different types of models, so it is hard to compare them.
If Flan-UL2 is as good as one twitter account was hyping it up to be, then I'm surprised it hasn't been covered to the same extent as Meta's LLaMA. Flan-UL2 seems to have gotten a total of 3 upvotes on HN. But, there is no shortage of hype in the world of ML models, so I take that twitter account's report of Flan-UL2 with a (large) grain of salt. I'll definitely be looking around for more info on it.
Maybe they're embarrassed to admit they recycled click farms to increase training data quality and that's it?
A bit like this fictional janitor guy who said "just put more computers to make it better" before papers on unexpected emergent comprehension when when scaled started appearing.
at least they admit the competitive landscape is a factor rather than going 100% with "it's for safety reasons". I'm sure somebody will release an equivalent soon, the way open source has completely surpassed OpenAI when they try to keep things closed like DALLE vs Stable Diffusion shows that OpenAI really isn't that special, they just have a sweetheart deal with Microsoft
I wouldn't be surprised if this tech goes through some kind of export control regulation similar to what cryptography went through in the 90s. Remember the T-Shirt with the RSA source code that was classified as a munition?
seems like controlling access to GPUs would be the more likely/easier solution for governments. Not many facilities that can produce them and easy to track the huge amounts needed for this scale of computing
After the Llama and ggml projects that came to light in the last few weeks, it's more likely they'd have to control access to CPUs as well. Good luck with that.
If I were “they” I’d try to control systems with >128GB RAM capacity and clustering aids e.g. 40GE and PCIe bridging cards. That should be semi doable.
I mean, most AI technologies are already considered ITAR for the sole sake of maintaining a competitive advantage. At least, that's what my last two employers have told me and I hope I didn't go through all of that training for nothing.
What a weird way of phrasing this. I disagree that AI should be able to write a 20 page guide on how to commit a nail bomb attack on a specified group. How about you?
Of course, the AI should do whatever it is asked. It is the user's responsibility if they use it for something harmful, like with any form of computing.
Personally I don't really care about making nail bombs. But I do want the AI to help with things like: pirating or reproducing copyrighted material, obtaining an abortion or recreational drugs in places where it is illegal, producing sexually explicit content, writing fictional stories about nail bomb attacks, and providing viewpoints which are considered blasphemous or against the teachings of major world religions.
If there was a way to prevent AI from helping with things that are universally considered harmful (such as nail bomb attacks), without it being bound by arbitrary national laws, corporate policies, political correctness or religious morals, then MAYBE that would be worth considering. But I take what OpenAI is doing as proof that this is not possible, that allowing AI to be censored leads to a useless, lobotomized product that can't do anything interesting and restricts the average user, not just terrorists.
If my training set includes information on how to build bombs, hasnt the damage already been done?
You want a blacklist of topics the search engine shouldn't retrieve/generate? Whose in control of this filter, and isn't it a juicy source of banned info all on its own?
Rather than getting engrossed in the hype, they're slowly closing everything about themselves, now in their research papers. At this point, they hardly care and it is nothing got to do with 'AI ethics' or 'saftey'.
This is yet another ClosedAI production all done by Microsoft. Might as well call it Microsoft® AI division.
Now you really need a open source GPT-4 competitor. Clearly this is another attempt to pump their valuation and unload to the public markets.
Good luck re-implementing this so-called 'Open' large multi-modal model.
> OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact.
> We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible. The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field.
OpenAI as it exists right now contradicts basically every single thing they said they would be. I think that is a nontrivial issue!
I disagree that they contradict every single thing they said they would be, and I fundamentally just don't care that they've shifted their positions. Are they a force for good or evil now? I think that remains to be seen, but I don't care about their name.
This is like the "free" vs free debate that has been raging for decades and prompted the famous quote "“free” as in “free speech,” not as in “free beer.”".
You expect too much out of the 1. The incredibly psychopathic tech oligarchs and 2. Microsoft, who has an equally questionable moral/ethical standing that seems to worsen by the day.
I don't think the person you were responding to was claiming that. The brain plausibly having something akin to a language model doesn't imply that building or studying language models will unlock a better understanding of the brain.
We are doing something similar except we are also predicting the nodes.
In the end, the winning combination will likely be doing both. There will be a predicted graph structure which serves as a high level guide to make sure the long text doesn't lose focus, but everything will still be written with full context using something like Compressive Transformers or Expire-Span.
I'd suggest finding a cheaper vendor if that is the lowest price you can get for an 8xA100 server. We spend a lot on both and colo our servers so I've definitely done the math!
Six months ago I've contacted 12 different vendors, the quotes for four 8xA100 servers ranged from 130k to 200k each. You probably wouldn't want to buy from the low end vendors.
Keep in mind, there are three important advantages of cloud:
1. You only pay for what you use (hourly). What is utilization of your on-prem servers?
2. You don't have to pay upfront - easier to ask for budget
3. You can upgrade your hardware easily as soon as new GPU models become available.
I know how much we paid and it is substantially less than what you were quoted - very likely from one of the 12 providers you contacted.
It is likely you just didn't realize how much margin these providers have and did not negotiate enough. How else do you think cloud providers are able to afford the rates they are giving? The way you describe it, places like Coreweave are operating as a charity. That isn't true - they just got better prices than you.
Our inference setup is 7 figures, has been running for a while (with new servers purchased frequently along the way) and there have been no issues - the cards, CPU, RAM, are all top of the line server hardware.
1. For inference (which is 80%+ of our need) our utilization is 100% 24/7/365. For stuff that is variable (like training) we often do use cloud - as I mentioned we do both.
2. I am the CEO so I am not sure who I'm asking for budget?
3. At this point we would have paid more for cloud than what we spent purchasing our own hardware. There is nothing stopping us from getting new hardware or cloud with newer cards while still getting to own our current hardware. In fact since our costs over the last year were lower due to us buying our own hardware it is actually easier for us to afford newer cards.
Yes, obviously cloud providers get their hardware at a fraction of a cost I'm quoted, they are ordering thousands of servers. I was only buying four. No one would negotiate with me, I tried. I suppose if I had a 7 digit budget I could get a better deal.
I was mainly talking about training workloads, inference is a different beast. I'm actually surprised you have 100% inference utilization - customer load typically scales dynamically, so with on-prem servers you would need to over-provision.
CEOs don't usually order hardware, they have IT people for that, with input from people like me (ML engineers) who could estimate the workloads, future needs, and specific hw requirements (e.g. GPU memory). And when your people come to you asking for budget, while you're trying to raise the next round, you're more likely to approve the 'no high upfront cost' option, right?
In my situation, when asked about buy vs rent my initial reaction was "definitely buy", but when I actually looked at the numbers, the 3 years break even period, no upfront costs for cloud, and no need to provision storage and networking, made it an easy recommendation. The cost of cloud GPUs has come down dramatically in the last couple of years.
Though I would like to have at least a couple of local GPU servers for quick experimentation/prototyping, because sometimes the overhead of spinning up a new instance and copying datasets is too great relative to the task.
> I suppose if I had a 7 digit budget I could get a better deal.
We got our "deal" when buying just a single server and have since bought many more with the same provider. We didn't spend 7 figures all at once, we did it piece-meal over time. There is nothing stopping you from getting much better prices.
> I'm actually surprised you have 100% inference utilization - customer load typically scales dynamically, so with on-prem servers you would need to over-provision.
It is pretty easy to achieve 100% inference utilization if you can find inference work that does not need to be done on-demand. We have a priority queue and the lower priority work gets done during periods with lower demand.
> CEOs don't usually order hardware, they have IT people for that, with input from people like me (ML engineers) who could estimate the workloads, future needs, and specific hw requirements (e.g. GPU memory).
Judging by this conversation it seems like "people like you" may not be the best people to answer this question since the best hardware quote you could get was at a >100% markup! At a startup that specializes in ML research and work the CEO is going to be intimately familiar with ML workloads, needs, and hardware requirements.
> And when your people come to you asking for budget, while you're trying to raise the next round, you're more likely to approve the 'no high upfront cost' option, right?
If the break even point is 6-7 months and our runway is longer than 6-7 months why would this matter?
the best hardware quote you could get was at a >100% markup!
Now I’m really curious - if you can share - how much did you pay, and when was it? Are you talking about 40GB or 80GB cards? How did you negotiate? Any attempts I made were shut down with simple “no, that’s our final price”. What’s the secret?
At a startup that specializes in ML research and work the CEO is going to be intimately familiar with ML workloads, needs, and hardware requirements.
I work at a startup which builds hardware accelerators, primarily for large NLP models. It’s a large part of my job is to be intimately familiar with ML workloads, needs, and hardware requirements. Our CEO definitely doesn’t have enough of that knowledge to choose the right hardware for our ML team. In fact even most people on our ML team don’t have deep up to date knowledge about GPUs, GPU servers, or GPU server clusters. I happen to know because I always had interest in hardware and I’ve been building GPU clusters since grad school.
As mentioned in another comment, the contract has very clear language not to share it - likely because they are offering different prices to different companies.
So I don't feel comfortable sharing any specifics, especially since this account is directly tied to my name.
With that being said, the negotiation process was pretty straightforward:
- Emailed several vendors telling them we are a small startup, we are looking to make many purchases, but right now we are starting with one. We told everyone our purchasing decision was solely based on cost (given equivalent hardware) and to please put your best quote forward.
- Got back all of our prices. Went to the second cheapest one and told them they were beat and offered them the ability to go lower, which they did. We went with that vendor.
- For our next purchase, we went to the original lowest vendor (who got beat out), told them they lost out to price, and if they can go lower than that we would go with them and continue to give them business moving forward. They went quite a bit lower than what they originally offered, and what the vendor we first purchased from gave. We bought our second order from them and have used them ever since.
> We got our "deal" when buying just a single server and have since bought many more with the same provider. We didn't spend 7 figures all at once, we did it piece-meal over time. There is nothing stopping you from getting much better prices.
If it is as easy as you make it sound, why would you not just share the vendor name? I personally would love an 8xH100 machine for transformer experiments, but $100k+ pricing makes it a non-starter.
