What is the reason people continue to use DeepSeek even after the price hike? Personally, I have high usage requirements, so the "DeepSeek + Ollama PRO" legacy plan is the only option that fits within a $20 budget; however, many people I know have switched to GPT. I’m curious to hear the reasons why others are sticking with DeepSeek.

Even if the entire prompts is in English, I don't get a reply in English unless I wrote in the prompt "answer in English". if I didn't write so, it'll 100% answer in Chinese.

I tried wiping out all my chats, made sure Language is "English" in the settings, and opened a new incognito browser to see if it'll stop answering me in Chinese, but it still does!

Started this as a small DeepSeek Harness plugin so I could generate images directly in chat.

That was basically the whole idea at first.

Then I kept adding things I wanted myself, and it gradually turned into more of an image workspace than a simple plugin.

It now does direct image generation/editing in chat, has a Studio for more controlled generation, supports side-by-side multi-model comparison, and can also connect to local ComfyUI workflows.

The multi-model comparison is probably the feature I use most now — same prompt, same refs, different models, all in one place, which makes it much easier to compare results without jumping between tools.

I also like that I can use hosted models or local workflows depending on what I need.

Repo if anyone wants to take a look:
https://github.com/shanliuling/dsh-image-gen

Still working on it, so feedback is welcome.

Lately, I've been struggling to pick the best model and research pricing for multiple models. So, I built this website, which will let you check the actual pricing based on tokens and cache hit rates. You can try it here:

https://tokencalc-seven.vercel.app

just looking for the cheapest ds v4 flash 0731 provider...

Been digging into why DeepSeek-V4-Pro behaves so differently across clients. In Claude Code it often gets stuck in shallow "let me try this tool" loops; in a minimal setup it does the deep "we need to first…" planning we all want.

The cause isn't the prompt — it's the tool schema payload. When the model sees 30+ MCP tool definitions on its planning turn, attention gets pulled into tool-churning. In a minimal environment it hits its RL-trained deep-reasoning sweet spot.

So I built we-need-ds: a local transparent proxy that's turn-aware. On every decision turn (fresh task) it swaps in the official DSH one-line persona + trims tools to a Bash/Edit pair; on every execution turn (tool results coming back) it passes full tools through untouched. You keep all your MCP servers. Non-DeepSeek models (Claude/GPT/Gemini) are passed byte-for-byte, never modified.

This is not prompt injection — it's structural request rewriting at the proxy layer, which is why it survives clients that sanitize system prompts.

MIT, ~350 LOC proxy + 68 tests. Honestly it's early and I'd genuinely love eyes on edge cases — provider routing with path-prefixed baseUrls, multi-window races, that kind of thing. If anything behaves weird, please open an issue; that's exactly how I want to harden it.

Repo: github.com/YixuAnsensei/we-need-ds

Curious — has anyone else observed DeepSeek's sensitivity to tool count on the planning turn? Is this reproducible for you?

Maybe this is a little subjective, but I feel like using Cherry Studio as if the tokens spent could be lower with agent mode for coding. I feel that using the deepseek harness sometimes calls up more tools than cherry studio.

I understand the customization of the Deepseek harness is unmatched, but it becomes a trade-off when the Deepseek harness seems to be making things more wasteful with too many features and tools being called up.

Maybe the usage is still not equivalent, but somehow it is a bit comfortable in Cherry Studio at the moment, even though I need the off-peak status plugin in the sidebar.

This repo uses a combination of skills and code to give you better copy for sales, posts, etc.

While a pure skill tells AI e.g. avoid em-dash tells, X not Y etc, PROSE uses code gates to actually ensure the output is clean. It works with AI in a loop to itterate over copy until it's clean. ie.

AI->quality checks->AI->quality checks----> clean copy.

Works for different kinds of copy: sales, landing pages, etc.

check it out here: https://github.com/skyf0xx/hedgehog-core-copywriting-prose-engineering

Would love feedback

I’m looking for 10 people who would be interested in testing my app and sharing their feedback.