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DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

esengine.github.io

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Re: DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

#71
post #31

I'm not sure you need a "DeepSeek native coding agent" to take advantage of DeepSeeks cache, yesterday as the Codex quota usage issue still wasn't solved for me, I wrote a tiny little bridge so I could use DeepSeek V4 Pro via Codex, and seems most of everything I did was basically cached as far as I can tell: https://i.imgur.com/7eKn6wN.png (2026-05-23 Input (Cache hit): 39,123,200 tokens, Input (Cache miss) 1,692,28…

Opencode has really bad cache stability issues that they seem uninterested in fixing at the moment.

I can't confirm this. Having utilized Opencode for a large project over the past 10 months, with multiple models and agents, we've never run into such 'cache stability issues'."

Re: DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

#72
post #67
post #20

Earlier quoted context omitted.

For me, it's by far Deepseek. It's many times cheaper than competitors, and about as good as Sonnet 4.6.

I'd generally agree about Deepseek being as good as Sonnet - but I have extreme trouble with prompt compliance with V4 Pro in a way that I've never had with Sonnet. I'll tell it "find the bug, but don't fix it" or "please use this tool I just developed" and it'll ignore me a high fraction of the time. It's bad enough that I'm working on guardrails at the harness level because prompting appears to be useless. Do you h…

I have Opus make a fairly detailed plan, then Deepseek implements, and GPT reviews. With that setup, I have zero issues, probably because what you mention is handled (the plan keeps it on track and the reviewer catches any issues).

Now that you mention it, though, I have seen it do a few things that weren't in the plan. The reviewer caught them, though, so they didn't cause a problem, and it's so cheap that overall it's a massive improvement.

Re: DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

#74
post #65

Earlier quoted context omitted.

That'd be really easy to spot and also fix, most likely. Any open issue you could point us to, must surely been reported already?

> That'd be really easy to spot and also fix, most likely Ah, reminds me of good old "There are only 2 hard problems in computer science: cache invalidation, naming things, and off-by-1 errors."

> Ah, reminds me of good old "There are only 2 hard problems in computer science: cache invalidation, naming things, and off-by-1 errors."

You quip, but LLM KV caching (from the harness side) is quite easy: You get a cache hit on stable prompt prefixes, period. That means you want to keep the prefix stable, and only append at the end of the conversation. Made up example: Don't put the git branch name into the system prompt part (that comes first), as whenever the branch name changes, that'd trigger a cache invalidation of the entire prompt.

Getting this right requires some care to not by accident modify the prefix, basically, and some design on communicating the things that can change (user configuration, working dir, git information, ...).

Re: DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

#76
post #74
post #65

Earlier quoted context omitted.

> That'd be really easy to spot and also fix, most likely Ah, reminds me of good old "There are only 2 hard problems in computer science: cache invalidation, naming things, and off-by-1 errors."

> Ah, reminds me of good old "There are only 2 hard problems in computer science: cache invalidation, naming things, and off-by-1 errors." You quip, but LLM KV caching (from the harness side) is quite easy: You get a cache hit on stable prompt prefixes, period. That means you want to keep the prefix stable, and only append at the end of the conversation. Made up example: Don't put the git branch name into the system…

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Re: DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

#79
> The loop is append-only, engineered around DeepSeek's byte-stable prefix cache — long sessions hold 90%+ cache hit and input-token cost collapses to ~1/5. Terminal-first, leave it running.

AI marketing slop. This is how all models and coding harnesses work, isn't it?

The author claims (in another AI-written post):

> LangChain — along with every generic agent framework I checked — rebuilds the prompt every turn. Timestamps get injected. History gets reordered. Tool schemas re-serialize with different whitespace.

I haven't touched LangChain in a long, long time, but don't think any of the current harnesses, Claude Code, Pi, Crush, OpenCode etc do that except if you change configuration? Keeping the context stable for caching is a very basic principle and not a wild innovation.

This posing as DeepSeek-specific is also a mystery.

Re: DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost

#80
Click on the download page, it's hilarious. It has a lot of information about the "smart probe" on the download and it's a realtime probe you can rerun.

That's the pinnacle of AI slop over engineered garbage in my opinion. All of that information is noise.

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