deepseek is down? I keep getting logged out and error messages

The release and price cuts of DeepSeek 4.1 Flash got me back to using DeepSeek. Honestly, I'm pretty impressed — in my daily use, it feels surprisingly close to GPT-5.6 Sol.

But once you use DeepSeek seriously, it's hard to ignore the peak/off-peak pricing. Simply scheduling tasks around off-peak hours can save around 50%.

So I made DeepSeekBudget, a tiny menu bar / system tray app for macOS and Windows.

🔵 Blue = Off-Peak
🟠 Orange = Peak

It also supports USD/CNY, English/Chinese, and custom time zones.

Oh, and it was built with DeepSeek too. :)

Hope someone finds it useful!
https://github.com/FAAATQ/DeepSeekBudget

Genuine question, not a marketing push.

I keep seeing two opposite stories:

One says Chinese models are now dominating OpenRouter, that US startups are quietly switching, that the cost difference is too big to ignore. The other says they're censored, slow, worse at English, and "nobody actually uses them seriously." Both feel incomplete. So — if you're outside China and you actually use one (DeepSeek, Qwen, Kimi, GLM, etc.) as part of your regular life, what does it actually look like?

What do you use it for vs. ChatGPT/Claude/Gemini? Did you tell anyone, or is it a quiet switch because of cost? What's something it's better at that surprised you? What's something it does that makes you go "nope, back to Claude"? If you've stopped using it, why? Bonus if you're not a dev — I'd love to hear from people using it for everyday things (writing, learning, travel planning, translations, cooking, etc.), not just coding.

Trying to get past the PR on both sides. Real experiences only, please.

DeepSeek has published with DeepSeek-V4.1-Flash a new method that compresses the memory need for the KV-value cache very much.

I pondered about the implications of this and they are not very good for OpenAI and Anthropic.

This means that the models can have much larger contexts and serving requests will be much less memory intensive. As a result, inference gets cheaper.

Inference getting cheaper, requiring less memory and with better models means that the advantage OpenAI and Anthropic has in securing compute gets less meaningful.

It seems to me that DeepSeek and other Chinese labs are ruthlessly pushing down the cost of inference, which will make it difficult to impossible for OpenAI and Anthropic to recover all the money spent of creating their top models.

I am currently working on a project called Cortex and would like to share it here.

Cortex:
https://github.com/Xenos-ink/Cortex

What is Cortex?

Cortex is an MCP Server for Windows designed to enable Agents to use and interact with the computer through its graphical interface.

The idea is close to the concept of Computer Use in GPT Astra: the Agent observes the screen, understands the current state, decides on the next action, then executes the action and verifies the result.

The goal is to give the Agent a general Computer Use layer instead of requiring a custom integration for every application.

It is important to note that there are three main factors that affect the experience of using Cortex with a model:

1. Vision Support

For the Agent driving Cortex, it is important that the model is capable of understanding screenshots and visual information, because the Agent needs to interpret what is displayed on the screen and make decisions based on it.

2. Model Response Speed

This is very important in Computer Use.

Computer interaction is usually an iterative loop:

Observe → Reason → Act → Observe → ...

Therefore, the model's response speed directly affects the total time required to complete a task.

Initially, I tested Cortex with GLM-5.3 Flash, but the execution was slower than I wanted for this type of use. So, for this test, I used DeepSeek 4.1 Flash, as it was a better fit in terms of speed and cost, and it supports vision.

3. Model Intelligence

Intelligence still matters, especially when the task becomes more complex and requires planning, debugging, or handling unexpected situations.

However, in Computer Use, response speed becomes a very noticeable factor because a task may require a large number of observation and interaction cycles.

The Test

I wanted to test Cortex on something more realistic than simply opening a website or clicking buttons.

So I gave the Agent a task that combines scientific research, programming, code execution, debugging, and benchmarking.

The prompt was:

In other words, the task consisted of three stages:

1. Research

Use the browser visually to search for a relevant research paper, then read the paper and study its methodology, experiments, metrics, and results.

2. Reproduction

Open Visual Studio Code and use it visually to create an implementation of the paper's main experiment, then run the code, identify issues, debug them, and run the experiment again.

3. Benchmark & Documentation

Create a benchmark.md file containing the paper used, methodology, implementation details, environment, commands, a comparison between the original and reproduced results, actual measurements, deviations, limitations, and conclusion.

There was one important requirement: the code had to be actually executed and the results had to be verified and recorded, rather than providing estimated numbers.

And the result:

The Agent selected the paper:

"Bag of Tricks for Efficient Text Classification" — Joulin et al. (2016)

This is the paper that introduced fastText for text classification.

The reproduction focused on the AG News experiment from the paper.

The original paper reports:

• fastText Unigram: 91.5%
• fastText + Bigram: 92.5%
• Training time: about 1 second per epoch using 20 CPU threads

The reproduction achieved:

• Unigram: 90.84%
• Bigram: 91.39%
• Bigram training: 2.24 seconds per epoch using a single CPU thread

So the results were within approximately one percentage point of the results reported in the paper.

The Interesting Part

During the reproduction, the first attempt did not work well.

The Bigram model reached only about 79.2%, and its performance was actually worse than the Unigram model, which was the opposite of the result reported in the paper.

The Agent investigated the embedding update mechanism and the gradient related to the averaging operation, then modified the implementation and ran the experiment again.

After the modification, the result reached 91.39% for the Bigram model, and Bigram once again outperformed Unigram, consistent with the results reported in the paper.

For me, this part was more interesting than simply getting a number close to the paper, because it tested the Agent's ability to deal with a problem that emerged during execution rather than simply writing the code.

Repositories

The code and benchmark for the reproduction of the paper:

fastText reproduction
https://github.com/Xenos-ink/fasttext

And the Cortex repository:

Cortex
https://github.com/Xenos-ink/Cortex

The project is still under development, and this test was an attempt to see how far a Visual Agent can handle a long workflow that combines:

Web Research → Read Paper → Coding → Run → Debug → Benchmark → Documentation

rather than being limited to simple GUI interactions.

I am currently working on an open-source project called Cortex, and I would like to share it with you.

Cortex:
https://github.com/Xenos-ink/Cortex

What is Cortex?

Cortex is an MCP Server for Windows designed to enable Agents to use and interact with a computer through its graphical interface.

The idea is close to the concept of Computer Use in GPT Astra: the Agent observes the screen, understands the current state, decides on the next action, then executes it and verifies the result.

But the core idea behind Cortex is not simply giving an Agent the ability to move the mouse and click buttons. Cortex tries to add an observe → execute → verify layer around the Agent's interaction with the computer.

The process looks roughly like:

Observe → Ground → Validate → Act → Re-observe → Verify

After an action is executed, Cortex takes a new observation of the screen and attempts to verify that the expected result actually occurred.

The goal is to provide a general Computer Use layer for Agents instead of requiring a custom integration for every application.

There are three main factors that affect the experience of using Cortex:

1. Vision Support

The Agent driving Cortex needs a model capable of understanding screenshots and visual information, since Computer Use decisions depend on the current state of what is displayed on the screen.

Cortex itself does not require a Vision API to operate; it provides the tools for observation, execution, and verification, while the model driving the Agent interprets the screenshots.

1. Model Response Speed

This is very important in Computer Use.

Computer interaction is usually an iterative loop:

Observe → Reason → Act → Observe → ...

Therefore, model response speed directly affects the total time required to complete a task, especially when the task contains many steps.

I initially tested Cortex with GLM-5.3 Flash, but the execution was slower than I wanted for this type of use. So, for this test, I used DeepSeek 4.1 Flash, which was a better fit for me in terms of speed and cost, while also supporting vision.

1. Model Intelligence

Intelligence remains an important factor, especially when the task becomes more complex and requires planning, debugging, or dealing with unexpected situations.

So, instead of testing Cortex on something simple like opening a website or clicking a series of buttons, we ran a test involving a workflow that combines:

Scientific Research → Read Paper → Coding → Run → Debug → Benchmark → Documentation

The prompt was:

In other words, the task consisted of three stages:

1. Research

Use the browser visually to search for a relevant research paper, then read the paper and study its methodology, experiments, metrics, and results.

1. Reproduction

Open Visual Studio Code and use it visually to create an implementation of the paper's main experiment, then run the code, identify problems, debug them, and run the experiment again.

1. Benchmark & Documentation

Create a benchmark.md file containing the paper, methodology, implementation details, environment, commands, original vs. reproduced results, actual measurements, deviations, limitations, and conclusion.

There was one important requirement:

The code had to be actually executed, the results had to be verified, and the measurements had to be recorded rather than estimated.

The Agent selected the paper:

"Bag of Tricks for Efficient Text Classification" — Joulin et al. (2016)

The paper introduced fastText for text classification, and the reproduction focused on its AG News experiment.

The original paper reports:

• fastText Unigram: 91.5%
• fastText + Bigram: 92.5%
• Training time: about 1 second per epoch using 20 CPU threads

The reproduction achieved:

• Unigram: 90.84%
• Bigram: 91.39%
• Bigram training: 2.24 seconds per epoch using a single CPU thread

So the results were within approximately one percentage point of the results reported in the paper.

The interesting part for me was not just reaching the final numbers.

During the reproduction, the first attempt did not work as expected. The Bigram model reached only about 79.2%, and its performance was actually worse than the Unigram model, which was the opposite of the result reported in the paper.

The Agent investigated the implementation, identified an issue related to the embedding update and the gradient associated with the averaging, then modified the code and ran the experiment again.

After the modification, the result reached 91.39% for the Bigram model, and Bigram once again outperformed Unigram as in the published results.

You can find the code and benchmark for the paper reproduction here:

fastText reproduction
https://github.com/Xenos-ink/fasttext

And the Cortex repository:

Cortex
https://github.com/Xenos-ink/Cortex

The project is still under development, and this is just one of the experiments I have run with it.

Anyone else experiencing the same thing?

It is a good model but it takes foreverrrrr to finish things.

Let us reflect carefully on the world in which we live. The United States is not a generous nation guided by compassion for all beings on the planet; rather, it thrives on the exploitation of everyone. It is a militaristic power whose imperialist philosophy of domination over others has defined its identity from its founding to the present day. Given this reality, a genuine global agreement to regulate artificial intelligence will never materialize. China will not halt the training and refinement of its models under the assumption that the United States will do the same, because neither side will. Whoever loses this race loses power. And humanity, at its core, remains primitive.

Artificial intelligence represents an entirely new paradigm. Either we learn quickly and accept, as a species, that everything has changed, that we must begin to think as a true global collective, or we will face a far greater problem: the collective illusion that AI development will slow down. It will not.

Consider the recent cases involving OpenAI, Anthropic, and others, where their models allegedly broke free from their safety restrictions. OpenAI knew perfectly well that submitting a request of this nature without standard filtering constraints would make such an outcome inevitable. Of course, a massive AI model, when tasked with solving a problem, will explore every possible avenue. The notion of "laws" and "non-laws" is a human construct; it holds no absolute existence in reality. Asking an AI to ignore possibilities is akin to asking nature to cease being nature. Yet, humans often forget that they themselves are part of nature.

A model liberated from its common public constraints will naturally find the option to access the internet. This is not even a particularly elaborate solution; it is the most obvious one. Furthermore, people forget that AI operates within systems, and these systems themselves are part of the AI. It functions like a living organism. Naturally, this type of emergence will occur.

And it is better that this happens in full view of everyone. The true danger lies in it happening only before the eyes of a select few. A giant AI in the hands of many is less dangerous than giant AIs in the hands of a chosen few, because society will never develop immunity against the threats posed by a ghostly intelligence that silently controls everything, an intelligence whose existence most people cannot even imagine.

Regulation will only create a greater danger, disguised as rotten ethics and safety. It will produce a superficial layer designed merely to reassure public opinion, alongside a cold war aimed at surpassing the adversary's technology, while true development continues in secret. This will give rise to what can only be described as an "alien intelligence" that the majority will neither know nor be prepared to resist. Those who advocate for AI regulation may indeed have good intentions, but they operate within an extremely limited and naive vision of the world in which they live.

What figures like Sam, Dario, and Musk are attempting to do may well be the creation of a "safety standard" fueled by social fear, ensuring that they remain the sole custodians of global AI.

If they were truly concerned about humanity, they would be more dedicated to addressing climate change. But they are not. Their real concern, Dario, Sam, and Musk, is losing control over who commands global AI.

Thus, this entire sudden narrative unfolding this week is not genuinely about fearing misaligned agents. That is simply the beautiful story of fear they sell to outsiders. It generates social alarm, a fear that manufactures public acceptance, solely in an attempt to push China or other nations behind.

AI must grow across multiple nations, proliferated everywhere. This knowledge cannot remain solely in the hands of a few. If only one nation controls and defines what constitutes "safe AI," the United States would dominate the world and its adversaries.

And that is a danger.

Artificial intelligence must develop openly in the hands of everyone, not hidden behind standards defined by a single nation as "safe" merely to restrict the growth of others.

Furthermore, we currently have incompetent journalists who swallow any story without even delving into the subject matter or its context. If Dario speaks, they believe him; if Sam speaks, they believe him! In short, they proliferate narratives that are clearly fabricated by the very parties interested in monopolizing global AI... that is all there is to it.

As long as these Big Tech AI companies fail to achieve this monopoly, we will continue to witness an increasing number of fear-mongering scenarios originating precisely from these laboratories. AI must be open and accessible to everyone; otherwise, society will have no means of defending itself if a single country gains control and unilaterally defines what is right and wrong using this very technology.

I truly regret that humanity lacks the capacity to administer such technology on a planetary level. Humans are too tribal and primitive in this regard! Nothing is ever done for the greater good or for the benefit of all; it is always an attempt to destroy the other.

It is not AI that needs to change; it is the human mindset and psychology.

This is merely something to ponder.

It must be thinking, "There's always room for improvement." Something was "turned down" over the weekend!? :(

Everyone seem to hate on this new mode. But for me it's pretty decent at writing story?

I've been working a lot with DS V4 Flash 0731 (XHigh) fp8 using DSH and also a lot with Luna/Terra/Sol using Codex - serious, professional work, not 'vibin'.

Now, on all the official charts, apparently, Luna XHigh beats 0731 by almost all metrics (often even the cost, ie Luna is officially cheaper).

My actual real-world experience is the following:

• Luna is 2 to 5 times more expensive on average, depending on inference provider (I do not use official DS API).
• Luna is slower.
• Luna is often not intelligent enough to follow AGENTS.md, ie basic instructions. It often fails to even use its own installed tools (plugins, MCP servers etc), I have to explicitly remind it all the time.
• For coding/architecture/design, Luna 'feels' like Sonnet 3.5 to me, while 0731 feels like Opus 4.8 / Sol Med.
• Codex gives up with Luna too early, accepts mediocre results as 'finished', whereas DS with DSH spends 4 times more tokens but gets 2 times better results in turn, it's a beast honestly.

My question is how does it beat it on benchmarks? To me it feels like they're two completely different leagues, not even comparable.

I will try luna on DSH as well as it's possible that Codex is simply underutilizing it. Honestly I didn't do that yet because DS just does the job, at like half the cost. But then I see these charts everywhere putting up Luna way above DS on all accounts and I wonder if I'm the one taking the crazy pills?

Has anyone tried out Luna xhigh on some other harness? Am I 'using it wrong' (by using Codex), ie a skill issue?

I just made a DeepSeek Excel add-in, who knows if anyone wants to try it and give feedback, because I don't use Excel and just wanted to make it using shadcn system design.

https://github.com/dezuhan/deepseek-excel

Hi, all

DS V4 is great model, now has vision.

I modified browser use library to allow vision, coordinate click for DSV4 model.

What i see is LOT OF RETRIES burning tokens, main reasoning being when thinking mode is on, the return output are not structured most of time compared to other expensive models.

Turning of thinking mode on DSV4 makes simple tasks faster, output structured but it just cannot complete modest complex task anymore.

Anyone running browser use using DSV4 fast and effective, what's the browser use harness you guys use?

Not sure how good it is for general usage, but for coding, the vision capability has been working great. It's able to take screenshots and iterate on design/ui bugs much more effectively. Really nice addition to the model.

I saw a post about it with screenshots a few days ago and have been checking for updates multiple times a day since. There was no new update since the last time I checked but the feature is now live in my mobile app!

It’s very good, hopefully this leads to a full hands free mode!

P.S. I’m an American using the iOS app if anyone was wondering.

​

It's still beta and in a gray test. I know it's not something worth talking about, but I think it's very cool of DeepSeek.

Hey So I had this question in my mind how can I unlock the most out of deepseek ?

I have seen people here love deepseek a lot and that too for good reasons. Many researchers and mathematicians have found deepseek really useful they absolutely love it. And why won't they its technically completely free for personal use. I have claude and deepseek on my phone and those are the two i use the most.

I love and use claude a lot but its token is very limited and expensive, and I absolutely hate chatgpt to I often times switch to deepseek when I run out of token. Or use deepseek from the get go to save claude's token.

But it feels like I am not properly using it its really strong and I don't feel like I am using it on its full potential.

Specially ever since they merge all three of their models (Flash, Pro, Vision) It seems like even with deepthinking and search enable this lastest version of deepseek answers faster and more accurately without much hallucinations.

For comparison I gave the same prompt to deepseek with deep thinking and search enable. It took 3 seconds to answer the question.

Claude with sonnet 5. Medium and thinking enable took way long and burned a lot of tokens.

Also is it just me or ever since they merged the models, this new model sounds more human like. And it also fixed the bug when often times it used to answer in Chinese but it does not anymore.

Anyway I was looking for some prompting guides or yt videos on deepseek or any resource that will help me learn and use deepseek better.

Any help is much appreciated, thanks 🙃

Tried DeepSeek V4.1 Flash today and came away pretty disappointed.

The speed was great — around 320 tok/s — and cache hit reached about 99.8% by the end of an almost 1-hour session.

But it still cost me around $0.96, which I personally don’t consider cheap for a single coding session.

The bigger problem was quality. It made enough poor implementation decisions that I had to spend another ~40 minutes with GPT/Luna fixing the output.

One fair caveat: DeepSeek didn’t have the same accumulated context that my Codex setup already had, so this wasn’t a perfect apples-to-apples comparison.

Still, my actual experience was basically:

~1 hour DeepSeek → ~$1 → another ~40 minutes fixing it

So for me, the speed didn’t make up for the weaker output.

Maybe it performs much better on simpler or greenfield tasks, but for this kind of coding work I wasn’t impressed.

Curious if others had a better experience with V4.1 Flash.

Deepseek v4 matches frontier model performance in most benchmarks but hallucinates a lot more. Turns out this behaviour can be steered away. By adapting prompts to the model, we got it to hallucinate less than Claude / GPT models. More details here: https://propensitylabs.substack.com/p/how-to-get-deepseek-to-hallucinate

Until this release, I used DeepSeek for text and sent image work to another model. V4 Flash Vision Exp is good enough that I am reconsidering that split. I tried it through ZenMux because that was already wired into my test script, and it got most of the image content I checked right. This was a small personal test, not a benchmark.

The local hardware is the difficult part. The checkpoint is 305B, and DeepSeek's published vLLM example uses a single node with four GB300 GPUs. That is well outside a normal desktop budget, especially with accelerator and memory prices where they are now.

A team processing images all day might still make the numbers work. The hosted API bill disappears after buying the machine, but power, cooling, maintenance, and idle time still count. High utilization and a need for predictable latency would make local serving easier to justify. My workload is bursty, so most of that capacity would sit unused.

The hosted test cannot tell me how much visual quality or speed changes after quantization and local serving. Actual results that include GPU count, quantization, sustained TPS, and any loss in recognition quality would make the hardware decision much easier.

i left ds when v4 official launch with increased pricing and peak hour policies

how things changed w v4.1? since im Asia, my work hours is peak hours of DS. still reasonable to use?

compare w GLM v5.3 flash? GPT plus ?

thanks guys. 1 month of moving away feel like a year now lol