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.
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!
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.
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.
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.
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.
The compiler can substitute the value how it sees fit. It's like #define, but type-safe and scoped.
Maybe it's folded into expressions, propagated through constant expressions, or used it in contexts that require compile-time constants (template parameters, array sizes, static_assert, other constexpr expressions).
I mean, not in this case of pi/2, where it's more about announcing semantics, but in general those are the purposes and uses.
It's along the same lines, but an NSL can be challenged in court (the FISC is a secret and lopsided court, alas). Companies like Apple and Google have fought specific orders publicly (and possibly some secretly), and some have won.
NSLs are also narrow in scope: they compel data disclosure, not active technical assistance in building surveillance systems like the Chinese law.
The Chinese laws can compel any citizen anywhere in the world to perform work on supporting state military and intelligence capabilities with no recourse. There have been no cases of companies or individuals fighting those orders.
A 2017 national intelligence law compels Chinese companies and individuals to cooperate with state intelligence when asked and without and public notice.
China has no equivalent of the whistleblower protection that enables resignations with public letters explaining why, protests, open letters with many signatures, etc. Whenever you see "Chinese whistleblower" in the news, you're looking at someone who quietly fled the country first and then blew the whistle. Example: https://www.cnn.com/2026/02/27/us/china-nyc-whistleblower-uf...
This is great advice and will give a good background in programming that mirrors what you would learn in a CS program.
I'd also like to suggest studying the practical side of building software that many university programs don't spend much time on. To help address this gap, John Ousterhout wrote A Philosophy of Software Design. He has retired from teaching, but captured the hard-won lessons in the book.
This type of book offers the perspective I wish I had developed more before working in software teams early on, as it would have made me a more valuable developer right off the bat. Instead, I went deep on architecture patterns and language theory, becoming somewhat insufferable to my peers (who were very tolerant and kind in return!) for the first few years. 20 years later, I can see that I was trying to hammer a CS "peg" into a business-software-shaped hole :)
Sure. Someone on /r/LocalLLaMA was seeing 12.5 tokens/s on dual Strix Halo 128GB machines (run you $6-8K total?) with 1.8bits per parameter. It performs far below the unquantized model, so it would not be my personal pick for a one-local-LLM-forever, but it is compelling because it has image and video understanding. You lose those features if you choose, say, gpt-oss-120B.
Also, that's with no context, so it would be slower as it filled (I don't think K2.5 uses the Kimi-Linear KDA attention mechanism, so it's sub-quadratic but not their lowest).
mail me at github at hella dot rip