AI OSS tool repo goes archived over night after raising $7.3M Seed
github.comThat being said, while I am biased, there is a lot of work around infrastructure so calling it "just a wrapper" massively underestimates the effort - this is purely from my own experience building this space.
Besides, if it is true how come OpenClaw is spending so much money on a open source project. Salaries alone will cost 7 digit sum for a harness and I have first hand experience dealing with companies doing exactly this.
Shameful plug - we are building cbk.ai, better known today as chatbotkit.com.
Their website landing page is now also showing the software is no longer maintained. No mention of why they made this decision, my best guess is they burned through their seed money and were unable to attract further investments.
[0]: https://www.tensorzero.com/blog/tensorzero-raises-7-3m-seed-...
I’d bet on extreme irresponsibility.
or you're incompetent
That is indeed how the VC funding game is played. If you don't raise another round, you are dead anyway, so you spend down your seed round to try and justify that following round...
PS: Someone won't become a trillionaire with this attitude.
"infra is safe" Hmm, but that wasn't a good idea. because if an open source infrastructure project like TensorZero gets shut down this quickly, won't they start to realize that those investment theories are also risky?
The difficult thing about AI infrastructure is that, unlike other industries, it will not become fragmented. It will likely remain tied to specific big tech models. What does this mean? It means that because AI models are not yet standardized, the infrastructure itself is actually riskier. In other words, the privatization of standards is happening.
The challenge with AI infrastructure is that an independent, stable standard layer has not formed, unlike in other software infrastructure markets such as databases, web servers, cloud, and containers. Over time, those ecosystems developed relatively standardized interfaces and operational layers. But the LLM ecosystem is still evolving rapidly. Models themselves change fast, APIs differ, pricing differs, context windows, tool calling, structured output, evaluation, fine tuning, caching, routing, everything keeps changing.
So even if an infrastructure startup tries to build a common abstraction layer across multiple models, before that common layer can stabilize, big model or cloud providers like OpenAI, Anthropic, Google, AWS, or Azure can just absorb the same functionality directly. In the end, AI infrastructure is at high risk of becoming an attached feature of model providers rather than solidifying as an independent layer.
But if a startup that raised 7.3 million dollars fails this quickly, who would trust and invest in such things? That aside, it seems AI startups are all the rage these days. I also want to learn AI and get funded like that. Does anyone here trust me enough to invest? About one hundredth of that would probably be enough
> are all the rage these days
Are they? Overall it seems kind of tame compared to 2020-21 since VCs are somewhat risk average outside of a few outliers. Funding looks much more concentrated these days.
I agree that most people misunderstand the concept of a 'moat' and become obsessed with that misunderstanding. People tend to think that only technical 'coding skills' which they can easily understand constitute a moat. But in reality, the moat is the entire workflow across the product's lifecycle, including coing skills. In that sense, infrastructure workflows are nothing more than 'the most easily replaceable consumables.' The essential purpose of infrastructure is to pursue 'standardization,' which paradoxically means a state of 'zero switching costs' where customers (app developers) can switch at any time to a better API or a big tech built in feature. Pure technology that doesn't latch onto the messy real world domains of customers will inevitably be absorbed without resistance by massive capital.
In some ways, customer lock in at the application layer, or even the fan culture around a product, creates emotional lock in. The end user app that provides a specific workflow integrated into users' daily routines can overcome even technical inferiority through 'experience' and 'emotion.' Technology can be copied, but the user identity attached to a tool is what I think a real moat is.(That is also the reason I love Windows.)
The example you gave, Cursor's Composer, is exactly the case I'm talking about. I think Cursor is inferior, and I don't think its Composer model feature is all that great either. But Cursor has a passionate fan base, and users who choose Composer as the best value for money no longer care about absolute technical performance or benchmark scores. They are captivated by the 'speed of experience' of code being completed quickly as they intended, and the 'frictionless workflow' the tool provides.it's not the company that builds the best AI model that wins, but the company that wraps 'good enough technology' in 'great UX' and dominates users' habits. That is how apps dominate infrastructure, and that's the moat you and I are thinking about.
That said, this conclusion is probably too hasty and has many flaws. Still, your thoughts are so similar to mine that I'm leaving this reply. Thanks for the great comment. Have a good day
> VCs think, 'Apps are risky, infrastructure is safe,' so they invested in AI infra.
First off, this isn't even infra in the infra sense of the word. Infrastructure implied something physical, a pure software product can almost never be considered 'infra'. A tool maybe, but not 'infra'.
VCs can also be irrational and driven primarily by personal connections rather than reason. I didn't do a deep dive in this project/leadership, but often who you know is some important than what you produced. There's a reason why a lot of VCs go for the old motto of "I'd rather invest in an A team with a C product; than invest in a C team with an A product".
A better model for VCs is: companies are finding tons of budget to allocate to new AI spend. Besides the labs, who is going to be able to capture some of that spend while they're actively looking to spend it?
Nobody at the seed stage is investing in things they think are "safe". They are investing in things they think have huge upside.
What you're talking about seems like 'ideal' investing, not real world investing at all. Of course, the VCs in your country and the VCs in my country are different.
It's like in software, where everyone says you should write maintainable code within the norms, but in reality, most people don't do that
that investing in 'potential' is the basic principle of VCs. They call it the power law. But when you look at actual investment portfolios, it seems quite rare for people to follow only that principle. I guess you don't think so. Of course, I agree that ideal venture investing follows the power law. But in real world investing, there are pragmatic investors who operate somewhere between the ideal and reality. We always project ourselves onto the 'ideal,' but I don't think there are only people who are immersed in that ideal. Of course, no VC would invest in someone like me. I've met with VCs three times in my career, but they all turned me down. Haha.
What I'm trying to say is that those success formulas themselves need to be reconsidered.An insider from up there came out and talked about the next 'Databricks,' believing that's the kind of potential they're looking for. All of them do. Everyone wants to be the first investor in a goldmine. I don't think this is just about greed
The question is whether the traditional infrastructure investment logic holds here. I think most current AI infrastructure tools are closer to 'temporary patches' that exist before the functionality gets internalized.
Let's say infrastructure is like a concrete building. Traditional IT infrastructure basically has a standards committee, and once that committee sets things, changes are extremely rare. It's a kind of 'lake.' But AI infrastructure right now is different from one to another; even the ecosystems differ—the Chinese ecosystem is different from the US ecosystem. It's a flowing 'river.' I just think the question is whether the old grammar can be applied in this situation.
You probably have more money, more investment experience, and more success than I do. I only have a lot of failure. But apart from that, the issue is simply that 'potential' in growth potential ends up being data measured against past examples, and the question is whether that data still holds up now. Anyway, I might have been slightly sarcastic earlier, so I apologize for that. Someone as successful as you, please bear with it a little.
(Also, we raised the capital in 2024 and didn't burn most of it.)
I mean it. I'm sorry once again
I think you're really overgeneralizing what "infrastructure" means in this case.
“TensorZero is used by companies ranging from frontier AI startups to the Fortune 10 and fuels ~1% of global LLM API spend today.”
One percent seems like a lot. Anyone on HN use this?
Ultimately I found the data model and UI to be both cumbersome and unintuitive. Langfuse ended up being the observability tool I went with instead over the one I built (and still use today).
The ~1% figure might be outdated today but it was a best-effort estimate a couple of months ago. TensorZero powered tens of trillions of inference tokens per month. TensorZero is not widely used but it was used by a couple of extreme-scale users.
Best of luck with whatever you do next!
Wasn't GitHub once a place for humans? Now we could rename it SkyHub.
It was a simple project in terms of technical complexity. I didn't publish it as I counted several similar projects in the field.
Putting $7.3M into such a project would make sense only in the case of a precise growth plan with already declared customers and an promising sales funnel. There is no technical moat.
> It was a simple project in terms of technical complexity.
That’s the thing, though. The version I build for myself sheds all the features that get in my way. I don’t share them either because they’re only useful for me.
Perhaps in the future big tech projects will be delivered with a common “core” and the expectation that agents fill in the use-specific stuff.
I feel like this is really going to change the software industry moving forwards. Historically it was tedious and time consuming to actually develop tailored dev tools which is why so many organizations relied on third party solutions. When nowadays you can easily half bake something in a few hours and get it working, tailored _specifically_ to your needs.
The thing is this requires you are given liberty to actually do this yourself. Think of something like say LMS software. Every college in the country is using what either blackboard or canvas. Could they make some bespoke LMS that works great for physics 101 at State university? Absolutely. But they don't, because the course director for physics 101 does not care or have the time to muck around with LMS prototypes. They barely have time to learn how to use their paid for LMS for anything but hosting the slides and syllabus.
So on the one hand, yes, there is massive creative potential for people to roll their own tools. But this is not often met with the required time and liberty to then go on to roll their own tools. Buying off the shelf still serves the organizational need it ever did: defer the creating to "someone else" who has been anointed by marketshare as the thing to do already, so that if shit really hits the fan you can just say you did what anyone would have done in your position. Same function as management consultancy: insulating fallout from bad ideas from the people who could be fired for it and give them essentially an out where they won't get browbeaten over it.
I think our culture around work and responsibility and "free time" needs a revolution for the LLMs to take off as promised as this playdoh tool.
That's way too narrow, though. It could be done at the department level, the university level, or the state level, right?
I suspect so, the headless / "api/cli only" tools like CRM are pretty big right now and I don't think we've seen the end of that trend, probably more like just beginning.
We started the company two and a half years ago, and raised $7.3m in 2024 (announced only almost a year later). We've spent less than half of this amount.
Earlier this week we came to the difficult decision to wind down the project. The open-source repository remains available on GitHub (Apache 2.0) but won't be actively maintained by the team moving forward.
I might publish a long-form reflection when the dust settles.
Very early in my career I used to believe that I or anyone else could be a CEO.
It wasn't until working with tiny teams where the CEO/founders devoted everything in their life to the business -- often at the expense of hobbies, romantic relationships, and any shred of free time -- that I realized true CEOs are a rare breed.
When are you ask things like "what happens if the product fails?" the answer would always be "It won't."
They both relentlessly believe in, and put every ounce of energy toward, their vision because anything less would not suffice
Again as trite as it sounds, I empathize with these people in that to them losing their vision felt like losing something dearest to them
In smaller startups, everyone is directly involved and has to punch above their weight to pull through, not just the CEO.
Also devoting everything in your life to one thing is not a mark of intelligence or skill. It is a mark of dedication but by itself means little.
And yeah, not everyone can be a CEO because most business fail very quickly. There is always an element of luck in those that survive.
But the idea that you devote 24x7 of your life hence you must be a good leader is not accurate. In fact, if you press this culture downstream, you'll tire your workers and the rest of the team.
For example: I cannot imagine being a successful touring live performer. I am an introvert, I keep a rigid schedule so travel throws everything off, can't keep myself awake very late...
Could I perform the functions of a live performer? Yes, though no matter how much I "tried" the mismatch between the job and my natural tendencies is a recipe for failure.
> not everyone can be a CEO because most business fail very quickly
These are not examples of in-born traits. While I agree that not everyone has the motivation to become a CEO, I would disagree that a person cannot learn and adapt.
Being a bit delusional is a critical quality of some CEOs, so the distinction hardly matters.
The problem with your point of view is that "Love of cocaine" is one of them, it's near the top, and you'll never acknowledge the fact.
> The problem with your point of view is that "Love of cocaine" is one of them, it's near the top, and you'll never acknowledge the fact.
I don't get why one can't easily acknowledge the slightly weaker statement that traits that are inherent to successful CEOs might be positively correlated to being prone to a love for cocaine (which says nothing about any causality).
This isn’t true. It’s easily shown to be not true by looking at all of the CEOs who had success with one endeavor and then failed all of their following startups, or the other way around.
A lot goes into founding a successful company. Not all of it is in anyone’s control. Not everything can be overcome by a CEO with powerful motivation.
Some times the market moves in ways nobody could have expected. I even worked at one startup that was destabilized and ultimately failed due to a natural disaster.
Looking back at the startups in my past, some of the worst CEOs were the ones who paraded around their ideals about failure not being an option or who pretended that they could get the company through anything through sheer force of their will and the power of their dream. One CEO who was all about “never give up, never surrender!” thinking ran the company into the ground because he refused to let us pivot after the initial idea didn’t get traction in the market but some other features were getting a lot of interest.
Some times knowing when to call it, move on to the next thing, and stop stringing your employees, investors, and customers along is an important CEO skill.
In one instance he raised the price of something by 1000 times without adding anything extra. His explaination was that it would build the right community. In his opinion people were to negative/sceptical and talked to much about what things cost.
Cost him 90% of the customers innitially then grew by 100ish%. As if some high end comedy the 90% said it was to expensive and that it would never work. The other 10% really needed to see what would happen.
Is there a way to reach out to you as I would like to hear what you have to say about what I am working on. You can update your HN profile to include contact information or you can reach out to me using my HN profile.
I'm basically working on a portable intelligence layer for AI that I will be open sourcing and the commerical product will be to make the intelligence layer even smarter. I can share the Show HN post that I am working on that better explains the value proposition and would love to learn any lessons you have gained while trying to sell AI tools commerically.
Edit: In case somebody calls me out it. I didn't want to use the `tensorzero` email domain incase the domain was going to become defunct soon.
and domains are cheap.
I’d shoot the note now if the feedback could be valuable.
The only way this could be a 'lesson learned' is if you homehow managed to not pay any attention to what has been going on in the last 25 years with open source software companies.
That's why it's called learning.
The other half goes where?
We are returning the remaining capital to investors.
Familiar with creditors getting divvied in bankruptcies, but not refunds to investors… oh it’s because there’s never any money left when things wind down. (We hear of retail stores where employees discover closures posted on shop doors when reporting to work.)
Early stage startups tend not to have a lot debt to pay off, because there aren’t many places willing to offer them much credit.
When after a few months we accepted that it wasn’t going to work, our investor got basically all his money back.
It was pocket change amounts compared to the sums of money that they deal with in Silicon Valley. But the point is the same anyway, the investor got back basically everything.
Ended up having to wind it down because it was a stupid idea and I realised quite quickly after spending money on it. Was a small amount of money but a lot for me. Luckily the investor never asked for money back.
Wound down my second one too but lost no money.
Then came into some money through a software sale about 7 years later, and offered to pay the first investor their full investment back, which was about half the money from the software sale (my only sale ever).
They really appreciated it but declined and instead said no, they want to invest in me AGAIN in the next one.
Felt really nice to have someone believe in you so much they would open themselves up to money risk again rather than take their initial investment back
It would be depressing if your first painting was your best work.
Honestly, I was close to flagging this story because the title is deliberately manipulative - it makes it sound like the founder did a rug pull. But I was really glad to see the founder come in to these comments and just say we tried, but the market shifted under us. Happens all the time.
The title is misleading unfortunately but that's how social media goes...
Major pivoting is almost always a really bad idea. (I admit I'm doing a bit of weaseling using the "major" qualifier, but when I searched for examples online, a lot of the ones that came back weren't major pivots, just slight refinements of focus to find better product market fit). Pivoting usually carries a lot of baggage - better to just give the money back and start afresh most of the time.
Now one question that you probably get a lot and I must ask: why not pivot?
News like this is reinforcing the narrative that frontier models and AI outsourcing generally is on the way down.
Treat it as an engineering problem where you're trying to lower the noise floor.
https://github.com/agentify-sh/gateway
For now I will just open this up to see if there is any interest if so I would be spending whatever free time I get to fix issues and open it up to other contributors so we can keep going.
I think its important to have an LLM gateway tool like this to remain open source.