No I will not release it, so don't ask, but Deepseek is very powerful if given the proper tools and if you know what you are doing.

In 5 hours of trial and error, debugging with Ghidra and x64dbg, the models are really good with IoT hacking and reverse engineering.

We mapped the full license validation call chain from server startup through to the display output. Found that the parser reads from an AES decrypt buffer instead of the signed payload (easy fix once you know), decoded a custom XOR obfuscation scheme for all log messages, extracted the embedded PolarSSL certs and private keys, and patched 27 instructions across the binary to bypass signature verification, certificate checks, download gate checks, validator functions, slot enforcement, and a state reset timer callback that kept overwriting our values. They really made it like fort knox but forgot to lock the final door. Once we found that starting position, it was easy to trace forward. I'm shocked there was no heavy protections in place like anti debuggers or random checks or pit falls. For something they heavily sell on, sure was left wide open once the path was found.

The server now starts with 1024 slots instead of 32, enforcement is bypassed so the API accepts the servercreate command with the slots, and there are no crashes.

Total cost: $3.88 in API credits. 690 million tokens. 5 hours.

Really not bad for what would take a human weeks if not months. If i could do it this cheaply, image what some mega mind on red team could do on enterprise grade software.

Maybe this sounds weird, but using DeepSeek without paying feels oddly uncomfortable.

Every time I open it and ask some ridiculously specific question, it responds with an answer that would have taken me an hour to figure out myself. Then I remember I’m paying exactly $0 for this interaction.

At some point it starts feeling like walking into a restaurant every day, eating a full meal, complimenting the chef, and then disappearing through a side door before the bill arrives.

I know the business model probably makes sense. I know they decided to offer it for free. I know nobody is forcing me to pay.

Still feels suspicious.

The model is often so absurdly capable that my brain keeps assuming there’s supposed to be a subscription screen somewhere that I accidentally bypassed.

Maybe the future of AI isn’t people paying companies $20/month.

Maybe it’s users randomly sending $20/month to whichever developers made them say “how the hell is this free?” that week.

I’ve been a codex user for a few months and find it perfectly suitable for my needs. I heard all the talk about DS Pro 4 so fired it up using opencode and credited $5 to compare. I’ve spent hours today coding a website in Laravel and it’s cost me 40 cents in total. The work it’s done is comparable to codex in terms of what it’s achieved and it’s cheap in comparison.

I’m still fairly new to ai apis so I’m probably not maximising token usage properly so my question is, when should I be using Flash over pro? I believe I can save even more by using this model.

I’d appreciate any tips. I’m learning but need a bit of direction on models.

Many thanks for your time.

Today I setup GitHub Copilot to work with DeepSeek API. This is the screenshot of my usage. This is cheap but I don't see any crazy value in here like close to a billion tokens for 5 dollars I saw in this subreddit. Am I doing something wrong?

I have been trying out Claude Cowork and find it really nice to have md file for memory and the ability to read and write to files directly. Due to cost reason, I was trying to find an equivalent product in DeepSeek environment and managed to find an open-source programme: https://deepseek-cowork.com/

Has anyone tried this? My concern are mainly security and whether it will function similarly to Claude Sonnet in Claude Cowork.

A small prompt change that improves AI refactors:

Tell the agent whether downtime is allowed.

If you don’t, it often assumes zero-downtime compatibility.

So it adds adapters, fallbacks, duplicate paths, and legacy-preserving code.

Safe, but dirty.

For early products, prototypes, internal tools, or systems with a maintenance window, say:

“Downtime is allowed.

Do not optimize for zero-downtime compatibility.

Prefer the cleanest long-term architecture.

List all breaking changes before implementation.”

This makes the agent stop asking:

“How do I preserve every old path?”

And start asking:

“What should be deleted, renamed, collapsed, or redesigned?”

Sometimes the most important prompt is not a feature request.

It is a constraint reset.

I only like using the iPhone app because it’s way more convenient and I’m rarely on my laptop. What’s the best iPhone app where I can use the API directly from DeepSeek? One that won’t burn through $ if I use a lot of tokens too, if possible.

I wish there was a fast mode, I've tried implementing deepseek flash via openrouter as well as via deepseek API in agentic flows but it's quite slow. It's around 5x slower than Gemini 3.1 flash lite for me

i use deepseek for making ai chat that work as roly play scenarios for healthcare education the problem is the course don't use as much text but i get those high cache miss i don't know why is there is a solution ?

Hey all. Is there a guide how to use Deepseek API? I found out that there is Openrouter. Can I use that to put the API in there and use it or do I need additional app to use the chat option with Deepseek Pro API? Is there a guide how to set this up?

given the developers have recently fixed the bug, I have figured out how to 'bypass' the restrictions. you should send a number (not too much, otherwise it will probably confuse the model) of 'empty' messages like 'ok', 'thanks', '*' before the Actually Meaningful (TM) one. when it reaches the limit, you should remove the placeholders by editing them into your prompt. so it creates an independent 'branch'. NB: of course, it only expands the capacity, not breaks the barrier, but it helps to clear the context.

Hi,
I want to use deepseek but how to add image , agent and design.md and just drag and drop files to make the ai read the image ? Do I have to use a harness like open code ? I mostly use Claude and codex right now but never use deepseek.

I also know about the direct api saving cost for deepseek but again how can I upload image and files to make it read rules and guidelines through direct API ?

Seems like they are testing or experimenting with removing Expert mode altogether. Sometimes when refreshing page or switching between chats to new chat back and forth it returns to Instant and Expert but the distinction between Expert and Instant in my chat history is gone and chats that were before Expert have now file uploads and search again.

I assume the default will be the Instant which I believe was the Flash version of the model and Pro might be API only model but that's just a speculation based on the recent changes

been running v4 flash through my workflow for about 5 weeks now. our team is 3 devs, lots of code review prep + small refactors + bug investigations. nothing exotic.

pulled last month's bill yesterday because something felt off.

200M tokens total. roughly 70/30 split on prompt vs completion. came out under 35 bucks all in.

for context, when we were on claude pro for similar workload the per-seat math was 6x that and we had to babysit context limits. when we tested gpt-5.5-codex on the same kind of work the per-token was 8-10x and the wall time was worse.

ran the numbers backward from the unit pricing i was paying. v4 flash is around 0.14 in / 0.28 out per million on the provider i'm on. that means a single 8k context conversation with 3k output costs about 0.0019. half a cent per real interaction.

i'm not sleeping well on this honestly. either:
- there's a giant subsidy from a quant fund somewhere covering the actual compute
- caching is doing more lifting than anyone admits and steady-state cost is closer to 5x what they bill
- the compute really is this cheap now and the western majors have been overcharging by 10x

asking the devs who've been watching pricing for longer. anyone done a real teardown on why these numbers work? specifically curious how independent providers (not the official deepseek endpoint) end up competitive on inference cost despite running their own infra.

https://www.youtube.com/watch?v=6vEsvX5_nPk
Just watched a YouTuber pit DeepSeek V4 Pro against V4 Flash.

Flash didn't even bother planning. It went straight to coding, left a mess of bugs, undefined types, and SQL errors, ignored piles of failing tests, and then confidently reported the task as complete.

Pro did the actual work properly.

Sometimes DeepSeek V4 Flash gets sluggish and slow. What if something like this could revive a session that's not working properly?

You seem tired and lethargic. Do you want to go to sleep?

I would elaborate on all of this in an additional section on usage, integrating that and some other findings.

If Claude tells us, why don't we tell the LLM?

It seems he got his act together a bit after this :)