diwank

"43 complete tensors exactly match name-seeded NumPy RNG output"

https://huggingface.co/burner22/numbat/discussions/13

watch this turn into a skill and become a popular meme on X...

you lose CoT monitorability which is a big issue since models have become quite powerful and also often deceptive but i do think that efficiency pressure will keep nudging us toward latent reasoning. looped language models are an active research area. but imagine not being able to monitor mythos' thoughts as it's working through a national security need...

this is surprisingly high delta. to make matters worse, reasoning tokens account for the majority of tokens and they are completely opaque so it's hard to tell how much of that is prose or code

i'm not happy with how openai is trying to pit 5.6 sol as a cheaper equivalent to fable here

for one thing, they said that on AA, sol is "within one point of fable" at 58.9 vs 59.9 but don't clarify that the latter is with safeguards where ~8% of the tasks got routed to opus

i'm not rooting for either and genuinely think that the token efficiency and cheaper price are important but this sort of thing just feels disingenuous :-/

where did you find that? weird coz their post announcing this also mentioned Claude Code:

> Fable 5 will be available starting tomorrow, Wednesday, July 1, to users globally on the Claude Platform, Claude.ai, Claude Code, and Claude Cowork. For Pro, Max, Team, and select Enterprise plans,1 Fable 5 will be included for up to 50% of weekly usage limits through July 7, after which it will be available via usage credits. We will re-enable access on AWS, Google Cloud, and Microsoft Foundry as quickly as possible.

https://www.anthropic.com/news/redeploying-fable-5

""" It remains unclear whether Anthropic’s engineers are assisting the NSA in active operations. However, one person close to the situation said Mythos would be useful for infiltrating the networks of nations such as China or Iran. """

in order for us to get there, i think we need a standardized api at the os layer for local models so that the os could optimize, batch and safely allocate resources. something like an analog of chrome's local model "prompt" api but provided and managed by the os itself. the user can choose which model they want to primarily use and so on but all of the heavy lifting and continuous batching is done automatically by the os

and device dependent. this is a very tricky thing to get rendered consistently

this is actually a surprisingly rich area of debate in philosophy of mind. see: https://plato.stanford.edu/entries/qualia-inverted/

had a bad experience with pg_search (paradedb) in the past

we have been using pg_textsearch in production for a few weeks now, and it's been fairly stable and super speedy. we used to use paradedb (aka pg_search -- it's quite annoying that the two or so similarly named), but paradedb was extremely unstable, led to serious data corruption a bunch of times. in fact, before switching to pg_textsearch, we just switched over to plain trigram search coz paradedb was tanking our db so often...

also shoutout to tj for being super responsive on github issues!

this is so disingenuous on symbolica's part. these insincere announcements just make it harder for genuine attempts and novel ideas

Angels & Demons anyone?

opus 4.6 gets it right more than half the times

Working on Memory Store: persistent, shared memory for all your AI agents.

https://memory.store

The problem: if you use multiple AI tools (Claude, ChatGPT, Cursor, etc.), none of them know what the others know. You end up maintaining .md files, pasting context between chats, and re-explaining your project every time you start a new conversation. Power users spend more time briefing their agents than doing actual work.

Memory Store is an MCP server that ingests context from your workplace tools (Slack, email, calendar) and makes it available to any MCP-compatible agent. Make a decision in one tool, the others know. Project status changes, every agent is up to date.

We ran 35 in-depth user interviews and surveyed 90 people before writing a line of product code — 95% had already built workarounds for this problem (custom GPTs, claude.md templates, copy-paste workflows). The pain is real and people are already investing effort to solve it badly.

Early users are telling us things like one founder who tracked investor conversations through Memory Store and estimated talking to 4-5x more people because his agents could draft contextual replies without manual briefing. It helped close his round.

Live in beta now. Would love feedback from anyone who's felt this pain! :)

I dont think this is Cerebras. Running on cerebras would change model behavior a bit and it could potentially get a ~10x speedup and it'd be more expensive. So most likely this is them writing new more optimized kernels for Blackwell series maybe?

yeah I agree. this is really unfortunate because it seems that there is something systemic here at play which has become twisted up in a cult of personality and that's made a rigorous scientific investigation very difficult