hobofan

Yeah, same. I jumped in via the website, and after driving around for ~5 minutes the main thing in my head was "damn, this is fun". And now, half my Saturday is gone...

I think the piracy scene has in many aspects always been romaticized to some degree.

With movies more so than with games, the piracy scene and the organized/commercalized bootleg scene were never far apart. Few people sit in a cinema with a camcorder "for the love of the game" and risk criminal charges, while many people will do the same thing for a few hundred bucks.

It's not

I'm not sure which provider you are paying for metrics, that you think has acceptable pricing.

We are on ~400k series, and switching to any hosted provider would instantly make that our single biggest infrastructure expense.

Instead we're self-hosting with less than $100/month for hosting and ~less than a hour per month of maintenance with Victoriametrics (have also switched fron singe-node to cluster over time), and it's a breeze.

The point of the feature flag tool is to control feature flags. They are not just for A/B testing and slow rollouts, and most products are not on the scale of Amazon, where you need to have a feedback loop for the rollout. Of course there is always going to be some overlap between tools, where many analytics platform allow you to do the rudimentary version of this.

The main point of a FF system is that you have a runtime-controllable mapping from user attributes to feature flags, where a set of logic rules can dictate who is exposed to what feature. With most analytics-tool based rollouts, you would be able to only directly set an attribute and you then hard-wire that attribute to the feature toggle in app code, which gives you a lot less flexibility and always requires a engineer in the hot path when it comes to doing rollouts.

Being able to point fingers is also a moot point from a theoretical perspective.

Given that every FF system comes with a bunch of different mechanisms that prevent your system from being catastrophically impacted in case the provider goes down, its still the fault of the integrating party if you experience an outage due to a FF provider.

What you are describing is usually part of analytics platforms (Mixpanel, etc.). A feature flag toggle only plays a small part in this (but it may also be used for a lot of other scenarios of how you may want to control feature flags).

Audience is decided by analytics tool -> Feature flag server maps audience to feature flag value -> Analytics tool tracks its events and includes feature flag value as attribute

You can pick a city and exact location on the map.

> and those tags/hashes will be changing all the time as the models evolve

I think that's where your assumption differs from stated claims.

According to Anthropics own docs[0], they don't alter the models at all after release. So for each model release you should be able to prove that a response was created with that exact model without dumbing it down.

Zero knowledge proofs also still allow you to have disclosed inputs. You can make one of the inputs be a publicly commited hash of the model weights, and have the ZKP validate that the inference was done with that committed set of model weights, in addition to all the inferences in the batch all coming from the same model.

[0]: https://platform.claude.com/docs/en/about-claude/models/mode...

If one were to listen to /r/claude or /r/codex where half the posts are complaining about models being "lobotomized", one could think that this is the most valuable thing there is!

In all seriousness, this is mostly just one of those defense in depth kind of things. Data exfiltration via a compromised provider is also a very different class of vulnerabilities than prompt injection via malicious tool calls.

That's two entirely different types of things that are being verified.

Watermarking checks whether a _specific text_ came from a provider.

PI checks that the text (that you already know came from the provider) was produced with _a specific set of weights_.

PI is mainly useful for AI supply-chain security.

I think you skipped over the "about 6 different features" part.

Everything you said only applies to unleashing an agent swarm on a single feature. You are not wrong on your assessment there, but working on multiple features concurrently does come with its own challenges.

It seems more like one or multiple suppliers of battery systems not taking into account that the decentralized deployments of their system could have that effect. If it's not regulated and an open market, this was bound to happen sooner or later.

For source coude to source code rewrites (which would be the case here when we talk about license laundering), AI is leagues ahead compared to binary to source code rewrites.

The "occupation map" and its current targets are exactly the opposite: services that either have a big support or maintenance component to them.

Especially during the current time of unprecedented pace of security patching, decrying this as "enterprise box-checking" couldn't be be more displaced.

No, that's nowhere in the definition of punishment. A system can be constructed with strongly normative attributes that punish anyone trying to not conform with the norm, without any intentionality behind it.

Whether it's on purpose or incidental doesn't matter.

"using a free browser" = "take any kind of privacy preserving actions"

If you use an odd/fringe browser you are more likely to be caught by CF Turnstile and be locked out from websites.

It's the same distinction as "open source" vs "source available" (when speaking about source code). It's not that widely in use because few people in the LLM space are diligent enough with IP to make the distinction, but the concept is easily transferable.

Yes, sodium polyacrylate is used in both expansion microscopy and baby diapers.

However you also need a few other ingredients to ensure isotropic expansion, or otherwise the expanded sample isn't scaled uniformly in all axis and you get a skewed view of the sample.

I find it fascinating that some of the main areas people are interested in are using benchmarks with quite few questions: ~200 for coding and ~150 for science.

As long as that's the case, it's no surprise that there is often a perceived difference of quality between benchmarks and real-world use.

I know that running the full composite benchmark set is probably already quite costly, and also that the benchmarks have to be rotated regularly, but it feels like a significant untackled problem.

Is your question "why don't decentralized humans appropriately self-regulate?"?

Presumably the more "real world" a breakthrough is the more integrated real world validation mechanism are required for an agentic loop to validate it's hypothesis (unless you have reliable mechanisms for simulation and throw even more compute at simulations).

At least that's roughly what the hypothesis of https://www.discoveryloop.com/ is.

> 3. Post IPO, will Open AI continue allocating similar levels of capital to further these causes?

I think the scarier alternative may be OpenAI forever gating their super-frontier models, capturing a significant portion of the sciences and leveraging it for equity in businesses that build on scientific advances. e.g. "Hey CATL, how about you guve us 5% equity in exchange for a battery chemistry breakthrough our AI has found?".

It's roughly equivalent to the playbook Palantir had for the public sector when they marketed themselves as a company that had cracked data science. At least that's what they've been pitching back then. I can't find a lot on public disclosed deals based on that.

For coding, no.

I have some business automation process for some customers that I maintain, where Terra with low reasoning ended up with the best price/performance & latency. With an equivalent Luna with high reasoning, price was usually better, but quality had a higher variance (very much depends on where the thinking takes the model) and latency for high reasoning was worse.

> well, Claude/Chatgpt is still marginally better for me now

Enterprise doesn't care about marginally better. They care about cost.

Most organizations that run our self-hostable chat platform with cloud models are for most stretches running 6 months-1 year old models, and are strongly eyeing (or have already switched) to more commodity models for most of their workforce. Frontier models are mainly relevant for engineering tasks.

I'm not asking him to open source anything. I've given up any illusions that that would be happening ~10 years ago.

I'm just cautioning other people to put any stakes into a language that's from its organizational bones doomed to fail for any outside party.

If he raises interest in the language by saying it will be open source (which he did), I think it's fair for people to dismiss the language until he does actually open source it.

Putting stakes into a language that will essentially be DOA, like the author does seems like a crazy decision.

This thing has the "the natural language nailed" to the same degree that a BERT has. That's a far cry from the intelligence level required to output text of any sensible length, and even farther from the structural adherence required for tool calls.

> uv sync

You can also just do "uv run", and it will automatically sync as necessary!

I think that's one of the things that uv does great and often goes unnoticed: It's hard to use wrong for a layman. Forgetting to run the equivalent sync command in most other Python package mangers could have resulted in a myriad of different bugs caused by out of sync packages, leading to hours of wasted debugging.