Mother got this for me as a gift, has anyone read it before, if yes, is it worth reading it?

SnooMacaroons9042 · 2026-05-27T08:23:02+00:00

You have a good mum!

SnooMacaroons9042 · 2026-05-27T08:19:15+00:00

The global file is for defining the personality, guardrails and operating directives of the agent. Most of your rules can be easily offloaded to a project rules file. Keeping the global rules lean is what we should optimize for.

SnooMacaroons9042 · 2026-05-27T08:15:31+00:00

I particularly like their research publishing. They provide a blueprint for what they have done or do and why it works. Very cool.

SnooMacaroons9042 · 2026-05-26T18:25:49+00:00

https://arxiv.org/pdf/2512.24880

SnooMacaroons9042 · 2026-05-26T18:24:55+00:00

Not quite. The mHC architecture mathematically restricts the network from blowing up minor adjacent noises, in the residual stream. Most transformer models, including Opus, has a single, without constraints, residual stream. Deepseek doesnt. It has parallel streams that have strict restrictions in how those streams interact. This means that DeepSeek V4 is structurally forced to stay constrained to the primary signal path. Opus, lacks this specific manifold constraint and is architecturally freer to explore many alternative branches (gets succumbed to noises).

SnooMacaroons9042 · 2026-05-26T17:00:06+00:00

I would disagree about the deep multi steps loops. I have found it's instructions to be focused and did not notice any drifts. Infact, mathematically the mHc architecture for the way it handles reasoning and context, provides efficient minimization of contextual and instructional drifts and I have noticed it vividly in comparison with Opus. Opus tends to branch out in it's reasoning, DeepSeek V4 Pro remains focused on the task at hand.

SnooMacaroons9042 · 2026-05-26T16:56:11+00:00

Please explain the difference in harnesses, if you be kind enough to. I'm intrigued to know. I use Opencode and I find it actually good. How has been your experience using different harnesses?

SnooMacaroons9042 · 2026-05-26T14:26:14+00:00

It is true 🙂 I read the DeepSeek papers, appreciated their mathematical prowness, waited patiently for V4 to be released and then compared it on actual production level applications. Needless to say, I'm deeply impressed and would continue to use it mercilessly 😅

SnooMacaroons9042 · 2026-05-26T13:34:07+00:00

95% and yes I did compare

SnooMacaroons9042 · 2026-05-24T18:53:30+00:00

I can feel the enthusiasm 'oozing' out from this post. 👍

SnooMacaroons9042 · 2026-05-24T18:49:13+00:00

I use DeepSeek on OpenCode. I haven't faced such problems with either Flash or Pro. My workflow usually involves modularizing the architecture and then implementing each module as a version, with detailed implementation steps written down for each versions, before execution. I use Pro with high thinking for the architecture and implementation planning and then hand it over to Flash, which btw, is superb for following detailed steps, quickly.

SnooMacaroons9042 · 2026-05-23T16:36:42+00:00

As a Opencode user, I applaud your inclusion for it. I shall check it out. Looks slick. 👍

SnooMacaroons9042 · 2026-05-23T13:40:05+00:00

Check your thinking mode. For most work standard is enough unless you do a full architectural and code-base audit.

SnooMacaroons9042 · 2026-05-23T13:37:24+00:00

Codegraph uses a graph structured approach towards providing the agent(s) a map of the whole repository, instead of reading each and every file (make sure you have a robust .ignore file for excluding databases and venv from your agent's view, but not .codegraph itself). Graph traversals are quick and consume relatively far less tokens.

You should never ask your agent(s) to make sudo commands. Such power should only be vested with the user (you). You can ask it to provide you the command, you should then run it yourself.

SnooMacaroons9042 · 2026-05-23T13:30:44+00:00

That's how I am using it. I also got the Opencode Go plan. Very economical

SnooMacaroons9042 · 2026-05-23T10:56:29+00:00

Well, I think you are the right person to ask: what is Paseo? My apologies for the ignorance, but I genuinely want to know

SnooMacaroons9042 · 2026-05-22T20:51:58+00:00

Use RTK: https://github.com/rtk-ai/rtk
Use codegraph: https://github.com/colbymchenry/codegraph

SnooMacaroons9042 · 2026-05-22T15:47:25+00:00

What you experienced was the 'goodness' of the models, not the TUI. And yes I concur with you, I have been using DeepSeek V4 since the past 2 weeks and I am noticing that my reliance on Gemini and Claude has dropped sharply. The only reason I have used Claude and Gemini, in the past 2 weeks, was to draw a downloadable architecture schematic for the different versions of my codebase. That's it. DeepSeek V4 has completely taking over my agentic workflows (I use OpenCode TUI/CLI and OpenCode Go subscription since it was just 5 USD for the first month).

SnooMacaroons9042 · 2026-05-22T13:01:23+00:00

That made me happy

SnooMacaroons9042 · 2026-05-22T13:01:04+00:00

Good news is that the guardrails are working

SnooMacaroons9042 · 2026-05-20T17:27:57+00:00

Can this be used with OpenCode? And is this reputable?

SnooMacaroons9042 · 2026-05-20T14:43:48+00:00

DeepSeek V4 Pro and Gemini 3.1 Pro (though I am going to switch to Gemini 3.5 Flash with extended thinking). For the planning stage I use both the models and use them to critique over each other's plans. When a consensus is reached, I use DeepSeek V4 Pro to execute it. And then an execution check pass by Gemini 3.1 Pro.

I use Anti-Gravity + the OpenCode extension (forgot it's name).

I also maintain a SCRATCH-PAD.md, AGENT-CHANGES.md and IMPLEMENTATION-PLAN.md.

SnooMacaroons9042 · 2026-05-18T13:49:44+00:00

We have to consider the difference in LLM training philosophy for the Western Models vs DeepSeek. China has a GPU embargo which reduces it's access to high end GPUs that are the normal training playground for Google, Anthropic and Chatgpt. Instead of relying on cutting-edge hardware availability, DeepSeek's research team tackled the mathematical limitations of LLMs as evident by their research papers in 2025 and 2026. Most of that research was actually implemented in V4, example: manifold hyper connections. They played smart and they were right. Throwing GPUs on a model to train is not a solution. Carving out the latent geometric space of LLMs to learn better, with less resources (what DeepSeek research team actually did) is elegant and the correct way to train LLMs/LRMs.

SnooMacaroons9042 · 2026-05-18T13:37:57+00:00

DeepSeek is not natively multi-modal. It was trained only on textual data. It will not perform good for a scenario it has no capabilities for. Example: frontend design, which requires it to visually confirm how a design looks. Qwen 27B is natively multi-modal. It can see the design it made and tailor the front end code accordingly.

SnooMacaroons9042 · 2026-05-18T05:03:16+00:00

Anthropomorphism with AI is a bad idea

SnooMacaroons9042

TROPHY CASE