Around 20 minutes into the flight, they deploy 20x v3 sats. Google search tells me those cost more than 1M USD each. Yikes... they burned up 20M USD for a "few tests". Damn, space is expensive! The official video says that SpaceX only had twenty minutes to test the deployed sats before they fall back to the atmosphere.
Not who you asked, but if you're looking for a rec, I've been using GLM and Deepseek models through Deepinfra because the prices are decent for US-based deployments and there's acceptable throughput and policy/terms. I'd love to find another that lowers prices over time when they roll out e.g. multi-token prediction instead of just taking a larger margin, but I don't know of any today.
Yes but there’s many reports of agents getting back testing to work but it never translates to real trading. It seems they tend to overfit to back testing so I’m just giving them access to Twitter sentiment, other trading data thru tools and not necessarily an algo that’s been back tested. It’s why the flash models are doing better image they have faster tps and can call the tools faster.
>they have faster tps and can call the tools faster.
This is like going to war with the HFT firms armed only with a stalk of celery ("it's much pointier than the tomatoes, even though those are more expensive").
Flash vs non-flash models are more about letting me know if model intelligence or model speed (with powerful tools/MCPs) are better for trading. I’m not telling the model to be fast as possible because HFT firms are already arbitraging those mini seconds to make pennies.
Alas, many financial models do well in backtests and then fail in the real world. You have to expose them to all kinds of market conditions and not just the recent one. Good luck out there!
Yes even before AI, backtesting was a crapshoot. But AI adds another crease because it might have knowledge in its training. If you’re training an LLM model on backtesting, it might know that Apple crushed a certain quarter and knowing that, it buys shares before that Q earnings.
And that is the reason why it is only autocomplete. You probably had less context than the poster before, so it could not mix stuff up.
The poster before either had more memory or the search searched through more topics. And btw it’s really hard to only give access to some things.
Yeah no human has ever made a mistake, good odds here! Luckily hubris is only a trait found in fictional stories and there are no real world accounts of people making terrible decisions while others go along with their terrible decisions.
Can't wait until we're forced to bail out these companies or else China will take over the open internet (or whatever bullshit they use to justify the bailouts).
I think where we went wrong in understanding this PR is in the assumption that it's designed to invite review because that's how a lot of other team- or community-driven projects work.
I think the point GP is making is this is a PR that smells like a solo dev working on their own project and not how a community-driven project adds major new functionality, although I'm sure there are docs and descriptions (or at least a discussion of tradeoffs and design decisions if not ADRs) are somewhere, but not linked handily to the PR. There is a lot of explanation in the blog post and PR, but it's unilateral-looking.
Redis was completely built in this way since the start. I believe this is a better way to create software. Compromise in design is, in my opinion, something to avoid: feedbacks are important, but often times a single person that studied a lot the problem and have design taste, can come up with a great solution. Mediating such solution, even among two stellar A and B solutions, will not produce a C soution that is better, since you can't produce such solution by interpolation. It is simpler to damage A and B. And: it is rare that in a big set of people all have stellar ideas, so you have to mediate, often, also with people having poor ideas. Not worth the effort for the way I'm wired. What works better for me is to provide hints about what I'm doing, then I receive feedbacks, and sometimes there are really great ideas in this feedbacks, and I incorporate the part I like.
Thanks, I think I'm all caught up now. The timeline is like this if I understand correctly: your successors (Yossi Gottlieb and Oran Agra) explicitly announced a new governance model in 2020, saying the project had "outgrown the BDFL-style of management" and that they wanted to "promote more teamwork and structure". With the relicensing in 2024, however, external contributors with five or more commits to Redis dropped to zero in the first six months (basically, community contribution collapsed). In late 2024, you came back in the role of "Redis evangelist" and a year ago there was an additional licensing change, adding AGPLv3 as an option (8.0's tri-license). So now redis has your steady hand on the wheel again.
I was confused because the last time I checked on things, it was still about fostering community input and advancement but not necessarily consensus. Things have tipped back in the original direction since then. I don't think "Redis was completely built in this way since the start" is completely accurate, but also the community effort under the new governance model never got very deeply entrenched while you were away.
It's looking rather low on reasoning and long-range problems with the approach described. For example, even with 16 agents and compaction, the HLE score is significantly below Anthropic's Mythos. Like you, I can see the release as a net Good Thing, but apples-to-apples for each org's latest models do have Meta holding steady in the middle pack.
HLE encompasses very hard problems where the larger pretraining of Mythos probably matters quite a bit. I'm not saying that Mythos is not showing some amount of genuine improvement compared to e.g. the latest Opus; just that if you're going to compare models, you should at least make sure that the overall test-time workload is in the same ballpark given how high it seems to be for Mythos.
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