▲ 5 r/ollama
When is GLM 5.3 available in Ollama?
Any ETA on ollama cloud subscription?
u/Dazzling_Yam_5882 — 6 days ago
▲ 65 r/kilocode
Well... Was fun while it lasted.
Going to Zoo Code instead 👋I knew from the point it got acquired by Anaconda things would start going downhill
For those that want a quick run-down on zoo code vs kilo code, the things that zoo code is missing from kilo code are: autocompletion (for those that still appreciate coding with minimum input from LLMs, although it doesnt really work in kilo...), commit messages generator button on the source control on vscode (I am implementing this feature on zoo code as we speak..), multi-agent window, a terminal/CLI interface and some other minor things. Zoo Code is open-source, not bought by a money grabbing company, and always improving.
u/Dazzling_Yam_5882 — 11 days ago
Harness is all you need? What are your favorite harnesses and for what models?
Image by Joel Niklaus u/HuggingFace, showing cost per task and harness being used. Just like there are websites that match the user hardware to the models it can run, would be interesting to have a website/tool that suggests harnesses for any given model, considering their differences.
I feel like we should have something like a "progressive" harness of some sort, adaptable to the model being used. What is the point of having all these harnesses if it is locked to just the top 10 or top 5% of models?
His text:
"Codex is overoptimised for large models: it ranks 2nd out of 10 for GLM 5.2 but drops to 9th place for Gemma-4!
Almost all the effort in this field goes into tuning the weights. We wanted to know how much of the final number is decided by the harness wrapped around them instead, so we ran 10 coding agent harnesses against two models on SWE-bench Pro.
A lot, it turns out. Swapping the harness moves pass@1 from 23% to 52% on GLM-5.2, and from 15% to 36% on Gemma 4 26B-A4B: a wider gap than most model releases buy you.
And the ranking does not transfer. The rank correlation between the two models' harness leaderboards is -0.05, which is to say: none.
Codex is not alone in this. Every harness shipped by a model vendor drops on the small model — Codex 2nd to 9th, Claude Code 3rd to 7th, Qwen Code 4th to 6th — while the model-agnostic ones climb: crush 7th to 1st, opencode 8th to 2nd, pi 9th to 4th.
The clearest case is crush, 7th on GLM-5.2 and 1st on Gemma 4. Run the identical scaffold on both and the small model wins by 4 points at a twelfth of the price, $0.30 per task against $3.61.
Gemma 4's best harness beats GLM-5.2's four worst. A 26B model in the right scaffold is not far off a 744B model in the wrong one.
Cost per solved task: $0.84 for Gemma 4 + crush at 36%. The cheapest GLM-5.2 setup that scores as well is openclaw at 38%, for $7.05.
Output tokens per task span 16k to 621k across harnesses: a 39x spread in what you pay for, buying a 2x spread in what you get.
97% of input tokens are re-sent conversation prefix, so prompt caching is super important.
The setup: every harness run against both models on the same 250 SWE-bench Pro tasks, one rollout each, priced at list API rates for the tokens each one actually spent. In the plot, a dark ring means the pairing sits on the Pareto frontier across both models and a faded dot means you could score higher for less elsewhere; 2 of the 10 harnesses (goose and hermes) are left out of the figure for legibility"
u/Dazzling_Yam_5882 — 13 days ago
DeepSeek doesn’t really want users. CEO calls them “sesame seeds, not watermelons.” AGI is the goal; the chatbot is a by-product.
Text by Tara Tan:
"DeepSeek CEO Liang Wenfeng’s leaked investor call is wild.
A few things that stood out:
• DeepSeek doesn’t really want users. Liang calls them “sesame seeds, not watermelons.” AGI is the goal; the chatbot is a by-product.
• DeepSeek could ~2x API prices without killing demand. It refuses to. Thin margins mean nobody can undercut DeepSeek using its own open weights.
• He says DeepSeek is 1–2 years behind the frontier but on 1/20th the compute. The goal: shrink the gap to 3–6 months.
• The next bottleneck is continual learning and he says nobody has cracked it yet.
• He thinks CUDA’s moat is weakening, partly because AI can now write the ecosystem code.
• He won’t touch video generation or world models. Commercially interesting, but “off the intelligence main line.” He thought everyone piling in after Sora was basically bandwagoning.
The strangest takeaway: DeepSeek looks like a product company, but Liang is running it like an AGI lab that just happens to have products"
What DeepSeek Isn't Doing - by Tara Tan
Maybe the reason why DS is increasing its prices and the communications seem so "take it or leave it". Will this impact your usage with DS models? What is your opinion on this?
u/Dazzling_Yam_5882 — 12 days ago
▲ 1 r/kilocode
Deepseek fash 0731 breaking
I have noticed that deepseek model seems to be breaking quite a bit with kilo. At some point in the run, it starts looping saying this:
DeepSeek Coder's FIM template is <｜fim▁begin｜> {p}<｜fim▁hole｜> — DeepSeek Coder's FIM template is <｜fim▁hole｜> — DeepSeek Coder's FIM template is `
And it just stops. It seems to me that something is triggering the reasoning to stop (some end token it seems). Is this something on kilo's side or deepseek side?
u/Dazzling_Yam_5882 — 14 days ago
▲ 6 r/kilocode
Kilo Code Extension vs CLI (VSCode)
Hey, awesome work with Kilo Code, it has been a really nice experience coding with it.
I do still have a question about the difference in experience/outputs in Extension and CLI. Is there any benefit on using the CLI vs the extension? I can paste images to the extension, but not to the CLI, so if using the extension and CLI provides the same type of harness/guidelines, then I would prefer to use the extension (comes more natural too, while having other terminals open, etc).
Thanks!
u/Dazzling_Yam_5882 — 1 month ago
▲ 1 r/huggingface
This won't ruin the open source ecosystem/put constraints on HuggingFace... right?
I believe Clem and the folks behind Hugging Face are doing good things. But the US speak in cash. Hope any "partnership" with the US won't ruin the good that Hugging Face has done to the open-source AI models. We do not want to see open source models behind some weird constraints or highly restricted to specific tasks/groups (looking at you Anthropic).
u/Dazzling_Yam_5882 — 2 months ago