DeepSeek just open-sources another piece of their training stack.
DeepSpec: a full-stack codebase for training and evaluating speculative decoding models
github.com/deepseek-ai/De…
"Value" is the transient order in the middle. "Fairness" is the attempt to redistribute the split after the fact, and it's never actually Pareto, because there's always someone (nature, the future, the outcompeted) who paid and didn't get a check.
Humans don't create, they transform:
extract low-entropy stock from nature
rearrange it into something other humans value
and dump the entropy and the uncompensated losses off-ledger.
We've kept hearing how GLM-5.2 beats Opus 4.8, and are skeptical of benchmarks - so we tested them on a real bug from the Cline repo. While both models fixed the issue, GLM was the winner in terms of cost and code quality:
- GLM used twice as many tokens (GLM 1.1m vs Opus 660K) but cost half as much (GLM $0.41 vs Opus $0.81)
- Opus finished quicker - 1.6 min and 12 tool calls vs GLM 4.7 min and 28 tool calls
- GLM cleaned up dead code and verified the build compiled before completing. Opus didn't - it left type errors that passed tests but broke the production build.
Both runs used the same Cline harness prompting and tools, so it seems GLM is RL trained to spend more tokens verifying its work before completing. Impressive work by the @Zai_org team!
@steipete Do we live rent free in your head? We all know what non profit means to people at OpenAI, nothing at all. Enjoy the millions you’re making from that non profit that hired you to pay you with VC money and an agenda.
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