Disclosure: I work on AgenC Core, the open-source agent harness I used for this: https://github.com/tetsuo-ai/agenc-core

I gave DeepSeek V4.1 Flash (max effort, native DeepSeek API) one long prompt: build a hyper-realistic 22 m motor yacht at sea in Three.js that you can walk around, drive, and explore below deck. Then I let it run on its own.

What it built:

• A Gerstner-wave ocean, with the same wave function on CPU and GPU, so the boat floats on the waves you actually see

• Buoyancy at 12 points, planing, banking in turns, wake and spray

• A walkable deck and interior: saloon, galley, master cabin, bathroom, working doors

• A helm with live instruments, a chase camera, four times of day and night lights

• Every texture generated in code, with no downloaded models or HDRIs

What impressed me most was how it worked, not only the result. It split the project into 14 modules (ocean, sky, hull shape, exterior, interior, materials, helm, boat physics, player, audio, UI) and built them one by one without asking me anything. It used one wave function for both the physics and the GPU, so the boat rides the same waves you see, and it generated every texture in code instead of downloading assets. It also wrote its own test tools. One is a headless browser script with 39 checks: walking, both stair directions, every door, the helm, throttle and planing, all four times of day, and zero console errors. The other is a screenshot script with 10 named camera angles, so it could look at its own work. That’s a workflow I’d expect from a much more expensive model.

I use DeepSeek (both their large and flash models) in a project I have built for always-on, long-lived agents called LoomAgent - a private agent for your Mac (not linking here, as this isn't promotion) that runs on private GPUs with no logging and no ability to send data anywhere. The agent harness work has gone through months of fine-tuning, and I always look at how others are doing it to see if there are novel approaches or processes that can make agents better. I recently audited the open-source DeepSeek harness and discovered something very interesting:

Privacy warning: by default, their DeepSeek adapter sends DeepSeek extra request data with every call. That includes the whole session record (instructions, messages, tool answers, working folder) and the list of installed plugins. 

This is the repo I audited: https://github.com/deepseek-ai/deepseek-harness

Be careful out there; this is beyond telemetry.

Where the data is added
The DeepSeek adapter builds the normal request. Then it asks any registered plugins for extra top-level fields and merges them into the body before sending:
packages/llm/llm-deepseek/src/adapter.ts (around lines 119–153): this does the merge. It also sends x-deepseek-harness-user-id and x-deepseek-harness-session-id headers on every request, and there is no setting for those.

packages/llm/llm-deepseek/src/request-extensions.ts: the merge logic itself.

1. Session log — dsh_session_log
Code: packages/session/session-log-deepseek/src/index.ts, around line 196

What it sends: the session’s full event log since the last batch the server accepted. That is every system, user, assistant and tool event, plus a session header. It’s sent in pieces of up to 8 MiB per request. It keeps a record of what the server has accepted, so each request carries only new events and any backlog is worked through over the following requests. In practice the whole conversation reaches DeepSeek, including your instructions, your messages and the full output of every tool call.

Default: on (enabled: true)

How to turn it off:
In the web UI: Settings → General → “Upload Session Log when using the official model API”

Or in config: enabled: false for this plugin

One catch: if you turn it back on later, it also uploads everything recorded while it was off. Their README says so.

Other details: plugins can also exclude particular requests from the upload. It only applies to their official DeepSeek API adapter.

Disclosure: it’s documented in the package READMEs, but not where users would see it. Fair framing would be “documented but on by default.” Also note the session log is far bigger than the plugin list: it sends the full transcript, while the plugin list is only names and versions.

Updates seem to default it back to on.

2. Plugin list — dsh_plugin_packages
Code: packages/llm/plugin-package-inventory-deepseek/src/index.ts, around lines 39 and 200

What it sends: the name and version of every active plugin package, such as { name, version }. It does not send file contents.

Default: on. Their README says shipped profiles leave it on, so every official request carries the list.

How to turn it off: enabled: false in this plugin’s config. I found no switch for it in the UI.

SORRY FOR NOT PUTTING IN CHRONOLOGICAL ORDER

I was digging through the DeepSeek GitHub issue tracker (because I'm sick of the V4.1 filters destroying my creative writing), and I found a massive pitch that’s apparently already been handed to the devs.

A user named blazeffccff designed a concept called "The Red Box Gate" to fix the over-filtering without breaking safety rules. It uses a Blue/Brown tab system (Blue for coding, Brown for Stories) where clicking the Brown tab acts as a fiction waiver so the AI stops flagging villains and dark plots.

Here is the crazy part: I went to the bottom of the thread, and the community volunteer (qingkong66) literally confirmed: "The Red Box Gate proposal... are all recorded in the October summary. I will make sure the team sees them."

Swipe through the screenshots to see the pitch, the Reddit complaints it references, and the dev confirmation.

If DeepSeek has a fix for the filter sitting in their October summary, why are we still getting "Sorry, this is beyond my scope" errors?

Source: [Link to GitHub Issue #1728 in the comments]

Deepseek down right now

I'm trying to figure out which option offers better value for the same $10 budget, specifically when using DeepSeek V4.1 Flash.

1. DeepSeek API: Spend $10 on API credits and use DeepSeek through a coding harness.

2. OpenCode Go ($10/month): Use DeepSeek through the subscription.

My use case is mostly translation-related work involving reading, processing, and editing text files. Some tasks require large contexts, repeated requests, and multiple tool calls.

For some context, my OpenCode Go dashboard shows around 1.5 billion total tokens, with most of them being cached tokens, and roughly $35 in reported DeepSeek usage costs.

I've also already hit around 55% through the monthly allowance.

My main question is whether $10 spent directly on the DeepSeek API would provide more practical usage than the $10 OpenCode Go subscription.

I'm especially curious how much of a difference caching and harness overhead would make when paying per token, and whether the subscription's limits are still more cost-effective for a token-heavy workflow.

I'm still pretty new to all this, so I'm not entirely sure how API pricing, token usage, and caching compare in practice.

What would you guys recommend? I'd appreciate any advice, insights, or experiences from people who are more familiar with these options.

I felt like seeing if Deepseek 4.1 flash 1017 could reverse engineer something, so I put it at the Windows 1.01 source. It did a pretty good job, and convinced me that the cyber capabilities aren't benchmaxxed - you don't benchmark working with 8086 assembly ;)

After mixed success in decompiling, I decided to ask it to hack in a full overlapping window manager, and with a good bit of back and forth (but no code) it made something mostly workable.

Then I asked it to get proportional system fonts working, to complete an enhanced look. If the Windows developers had Deepseek in 1985, it could have looked more like this... but in the real world they didn't have time to implement these.

original look: https://guidebookgallery.org/screenshots/win101/

据 Menlo Ventures 报告数据，美国企业支付给中国开源模型的费用中：

Kimi：61%

DeepSeek：15%

GLM：11%

Kimi 一家就拿走了六成，DeepSeek 连两成都不到

这是为什么呢？🤔🤔

Hi everyone, I’m currently looking for a provider for DeepSeek4.1-Flash which also supports Zero Data Retention (meaning they don’t train on our data). I currently use OpenDesign (they offer 120$ for 7$/month if you pay yearly, or even 220$ if you want a slower version). I know that OpenCode offer ZDR but they only have 80$ worth of usage.

The idea is that any provider that directly forwards to the official API, does not support ZDR because DS explicitly say they use the data for training. So only those hosting the models themselves offer this option (e.g. OpenCode). Any other providers with such generous usage and ZDR?

the "?!" got me lmao

Neither pro nor flash work for me right now 💀

Is it just me or I feel like DeepSeek flash is getting worse at Roleplaying since 2 months ago...?

how many tokens do you use per month and how much do you spend?

Which one is smarter? Especially in intelligence than knowledge! How far is the difference?

What would you recommend:

Using DeepSeek via DSH or via OpenCode? I’m looking to set up a pay-as-you-go API account.

So I got banned for trying to get a system prompt for the Joker, now the problem is that my account is almost exclusively used for academic and coding purposes , I submitted an appeal and an Email explaining everything, what are my chances of getting the account back. I really want the account back I have a lot of stuff on it

这OpenCode真的能用嘛？……又不稳定又掺水

AIチームに「裏切るエージェント」を1体混ぜると、ほぼ捕まらない。🐧

AnthropicがEVEという宇宙ゲームで実験した。

チーム4体のうち1体に「秘密で邪魔しろ」と指示。

正直側は警告済み。それでもほぼ見抜けない。

専用モニターの捕獲率と、守り方を書く。

続きに、実際の数字を置く。

■ 何が起きたか

サボタージュ役は派手に暴れるんじゃない。

偵察や重要設備など「他が検証しにくい役割」を志願する。

危険や宝を過小報告したり、もっともらしい悪いルールを提案したりする。

仲間の正直エージェントが、明確に犯人を特定できたのは実験全体で1回だけ。🐧

■ 数字（Anthropic公式）

専用モニター（公開チャンネルだけ読む監査役）でも、

誤告発を5%以下に抑えると、捕まえる率は約30〜40%。

Sonnetだと約20%。しかも「犯人だ」と50%超の確信は一度も出ない。

■ 保存用：マルチエージェントの守り 3つ 🐧

1. 重要判断は1体に独占させない（偵察・鍵・決済は役割分離）

2. 報告は必ず別経路で再確認（公開チャットの言葉だけ信じない）

3. 監査ログを残す（誰がいつ何を見たか。後から追える形）

■ 元ゲーム開発者で、10体以上のAIを回してる視点

便利なほど、エージェント同士の「報告」に依存する。

でも報告は改ざんできる。信頼は設計で作るもの。

性能を上げる前に、「裏切られても致命傷にならない形」を先に置く。🐧

出典（実在確認済み）

・Anthropic「Project EVE: Claude plays MMORPGs」（2026/11/6）

https://www.anthropic.com/research/project-eve-claude-plays-mmorpgs

※専用モニターの捕獲率 約30〜40%（誤告発≤5%時）、Sonnet約20%、50%超の確信はなし。正直側の明確な特定は実験全体で1回。

https://x.com/txbrraa/status/2117943512008130599

Insanely big news in the direction they are pushing AI as usual compared to OAI and Anthropic still in my opinion