ubutler

Rule #1 of rolling your own RNG: don’t.

Rule #2? Please don’t.

If anything, the latest generation of AI models, Astra and Fable, are prime example of overfitting—whereas benchmarks suggest they’re AGI-tier, users (including myself) report the same old gaslighting, hallucination, context rot, cheating, incomprehensibility patterns as with prior models, sometimes even more pronounced.

Fable and Opus 5, I suspect, will become textbook examples of RL collapse.

Just a word of advice, it could go a long way to have a human write the README rather than an LLM.

Right now, it’s a little hard to follow what the actual findings are, why they are what they are, and why that’s important.

> The six solid objects discovered on Forrest Beach, to the north of Townsville, are thought to be space debris, and the Australian Space Agency (ASA) is now trying to determine where they came from. The BBC has approached the agency for comment.

Off-topic but this is the first time I’m learning we (Australia) have a space agency :O

> Sounds nice except that these are 1 very small scale model, 1 reranker, and 1 embedding model that are far from frontier LLM level.

We've tried to take a first-principles approach to our end goal of 'legal superintelligence' that has involved identifying the areas of our domain most in need of improvement and releasing models that raise the bar on quality in those areas.

We've been around for a couple months now and ended up starting with retrieval and enrichment. The models we've released to tackle those problems have indeed been smaller in size than their competitors, yet they still rank ahead on open-source benchmarks.

Them being so small also helps with their accessibility — as I mention in our post, our models can be deployed on ordinary hardware, not a supercomputer.

Next on our roadmap is reasoning and research, which will require more infrastructure to support, but again, we aim to be judged by performance at the time of release.

> As much as I agree with the message, this reads like marketing copy trying to make a big deal out of a tiny model being hosted privately.

The point of this point is really just to reaffirm our commitment to sovereignty and accessibility and contrast our approach with that of major AI labs. It _is_ possible to commercialize LLMs while still keeping them accessible. A customer using a self-hosted deployment today does not need to worry about our models no longer being available tomorrow. We think that's a good thing. And moving forward, we want to keep that option available for anything we do, instead of trying to pull up the ladder while we're ahead.

Our models can be deployed on premises as well, though that is more of a bespoke offering at the moment. We've also been fortunate enough to have trained most of our models on our own private infrastructure. Your question raises a broader question, though, about sovereign risk attached to cloud services in general. For some, particularly governments, sensitivity is so high that certain workloads must run fully on their own hardware. I don't see that changing for now, and, in fact, in certain jurisdictions, the trend is turning against cloud services.

ChatGPT already does this, albeit in limited circumstances, through the use of its sandbox environment. Asking GPT in thinking mode to, for example, count the number of “l”s in a long text may see it run a Python script to do so.

There’s a massive issue with extrapolating to more complex tasks, however, where either you run the risk of prompt injection via granting your agent access to the internet or, more commonly, an exponential degradation in coherence over long contexts.

This is called a lexical innovation ;). https://en.wikipedia.org/wiki/Lexical_innovation.

We'd argue it makes a lot of sense to appropriate 'graphitization' as a term for a model designed to transform data into knowledge graphs.

> Also, you really want to tell people how to access it and what it costs. Or put up a "call for quote" if your market is large Enterprise budgets.

Our pricing page can be found in our documentation here: https://docs.isaacus.com/pricing/prices. We're planning on making it more visible on our website; thanks for the feedback!

FWIW we're planning on releasing a self-hostable version on AWS Marketplace quite soon followed by one on the Azure Marketplace. In both cases, deployments live entirely in your tenancy, are fully air-gapped (ie, they can't access the internet), and your usage is unmetered.

We do already have a government-facing client using one of our self-hosted deployments given the privacy and security concerns the legal industry tends to have (rightfully in our view) around AI.

> Weirdly, the blog announcement completely omits the actual new context window size which is 400,000: https://platform.openai.com/docs/models/gpt-5.2

As @lopuhin points out, they already claimed that context window for previous iterations of GPT-5.

The funny thing is though, I'm on the business plan, and none of their models, not GPT-5, GPT-5.1, GPT-5.2, GPT-5.2 Extended Thinking, GPT-5.2 Pro, etc., can really handle inputs beyond ~50k tokens.

I know because, when working with a really long Python file (>5k LoCs), it often claims there is a bug because, somewhere close to the end of the file, it cuts off and reads as '...'.

Gemini 3 Pro, by contrast, can genuinely handle long contexts.

Here's a copy of the only known recording of a prank call to the Queen: https://www.youtube.com/watch?v=-YFFhc3XZDw

After having read the article in its entirety, I’m still not sure what Cybersyn is…

Personally, I like it. However, I like being able to comment and upvote more. At the same time, I'd be reluctant to say the least to hand over my login credentials. It could be quite cool to see this turned into a FOSS RES-style browser extension. Or maybe even a commercial product. I already paid for the HACK app.