The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 23 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Awais: Claude Code hides 50+ tool-call failures per session on DeepSeek
“In CloudCode you know, they hide a lot of the errors behind control O, right? So you don't even know that, you know, you have like 50 plus tool call failures plus per session. You're just sitting there and you're like, oh, why is DeepSeq so slow?”
McGrath: DeepSeek Math's real breakthrough is verifiable reward trust, not GRPO
“As you said, it came out in the deep seek math paper, and like, it's an interesting optimization method, but it's like the more interesting thing that they have a new reward signal that they sort of like re that we can really, really trust. Like when, you know…”
Nair: OpenAI already possessed a superior model during the DeepSeek release
“The feeling in OpenAI is that like, well, I think we had a better model already at the time, right?”
Bakouch: DeepSeek and Qwen do not release all their ablation data
“We want to train our MOE because it's fun and everyone is doing that. And also I think there is a lot of different direction. And it's always good in terms of science. To, because basically the coin tree or even deep seek, they don't release, release all the a…”
DeepSeek's open releases pulled global AI progress forward by six months
“But I think that what it did is it pulled forward progress in AI by like six months.”
Hong: DeepSeek dissolved its formal reasoning team over strategic shift
“And we have since, for example, Deep Seek All right. Like originally having a formal team and then later dissolve that team because of strategic direction change.”
Morris: Weight deltas can reconstruct a competitor's proprietary fine-tuning dataset
“There's some tricks to it, but it's basically just like gradient based selection based on this weight difference. And it seems to be okay. Like it can get us pretty good training data. So I guess if you actually wanted to use this, it would be like your compet…”
Beauchamp: DeepSeek slashes inference costs by shrinking KV cache
“What DeepSeek have achieved that's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cache significantly. And then by being able to do that, they're able to significantly reduce their inference costs.”
Zhang: Baidu and ByteDance Internal Models Use DeepSeek-Like MoE Architectures
“As far as I know, some companies such as Baidu or Baidu Dance, they are internal, the dominant AOM, they use the MOE architecture, and their ways, I think, is similar to the DeepSeq MOE model.”
Huang: DeepSeek's Multi-Head Latent Attention is novel and underrated
“Underrated specific instance would be, like, the deep seek paper. I'd never seen it before, but, like, the multi-head latent attention, like, that was really unexpected to me because, like, I thought I'd seen every Not every type, obviously, but, like, every w…”
Mechanistic Interpretability Scales Without Bottlenecks to Large Models Like DeepSeek
“There's no gap for scale. Like, they've shown that even for the biggest open source models, you like, even like DeepSeq's big models, you, they can do it. And then in general, like, scaling is not the bottleneck.”
Agarwal: DeepSeek Distilled Reasoning Models Using Correctness-Filtered Synthetic Data
“What they did was they took the best model they had, they generated a bunch of samples, they filtered them based on correctness, because these were on tasks like coding, mathematical problem solving, and a bunch of those things. So they saw whatever, what are …”
Agarwal: Synthetic data distillation bypasses model vocabulary and tokenizer mismatches
“The one nice thing about this kind of distillation is it doesn't matter if you have a vocabulary mismatch, because we're not using the next token distribution or probability labels. You can distill from one model which uses some random tokenizer to another mod…”
DeepSeek Revealed Attention Kernel Optimizations Kept Secret by Big Labs
“Deep seeks recent open sourcing of their various code components that they use to train that model, which I think outside of the big labs was not really well known to, right, it was not really well known how to write kind of a kernel that's optimized for this …”
Colvin: AI providers are centralizing around OpenAI's API standard
“I think the truth is that everyone is centralizing around OpenAI's SD API as the one to do. So DeepSeek support that. Grok with a K support that. Olama also does it. Well, I mean, if there is that library right now, it's more or less the OpenAI SDK.”
Zhang: DeepSeek team officially recommends SGLang as its inference engine
“That's why SGLAN is the recommended LLM engine by the DeepSeq team.”
Lambert: Early DeepSeek and Qwen reasoning models are substantially narrower than o1
“And I think that these models are really substantially narrower than these full O-one models from OpenAI. So OpenAI is, if you use O-one, you can do it for a lot more tasks. If you use, like I was using the DeepSeq model, and it's supposed to be for math or co…”
Soldani: 2024 open models rival frontier performance of closed models
“You have models that are, you know, reveling frontier level performance of what you can get from closed models, from like Quen, from Deep Seek. We got Lama III.”
Awais: Claude 3.7 Max is already CommandCode's second most used model
“But they will only be for deep seek when to 3.7 max is the second most used model on command code right now. It's just two or three days old.”
CommandCode offers 600 million DeepSeek tokens for $1 per month
“We launched a Go plan with just dollar one per month to, where you can do like, six hundred million tokens of DeepSQL for Pro in it, just to prove like, open models are actually really, really good, and they are catching up, right?”
DeepSeek 128k context fits in 8GB KV cache versus Llama's 80GB
“For context like the total I think the total context length of DeepSeq is a 128,000 tokens, or it might be 256,000 with rope extension. That entire context, I think it's a 128,000, fits into eight gigabytes. And previously context, like I think the Lama four o…”