u/ANDRE_2512

The day has finally come — Syntropy is now open for beta testing.

For now, Syntropy is deployed on Cloudflare Pages so we can give users fast access to the product and test everything under real-world conditions.

Try Syntropy:
https://syntropy-app.pages.dev/
No applications. No long waitlists. Just open it and try it.

To use Studio, you’ll need an E2B API key. E2B provides the cloud Sandbox where the agent actually runs.

You can create your own E2B key here:
https://e2b.dev/dashboard?tab=keys
Or, for beta testing, you can use our free test key:

e2b_182cd9974492e56fd6281669039a8f0860cf764a

Paste it into Settings → E2B API Key. After that, you can launch Studio and use the available free Zen models, including DeepSeek.

Right now, the most valuable thing for us is your bug reports, criticism, and feedback.

Syntropy Beta is live. Let’s go.

Today I’m releasing the second beta of Syntropy.

The idea is simple: your coding agent should not require you to install a bunch of tools, keep your laptop running, or host the agent on your own machine.

As you can see in the demo, OpenCode runs entirely in its own cloud sandbox. Compilers, runtimes, dependencies, and other tooling are already installed and ready to use.

So there’s no:
“Run the agent on your PC and control it from your phone.”

The agent actually runs in the cloud.
Right now, the beta includes free Zen models, generous usage limits, and no paid subscription.
I’m currently looking for more beta testers, and a mobile version of Syntropy Beta is coming soon as well.

If you’d like to try it, leave a comment and I’ll send you an invite.
Feedback is very welcome - especially criticism.

Today I’m releasing the second beta of Syntropy.
The idea is simple: your coding agent should not require you to install a bunch of tools, keep your laptop running, or host the agent on your own machine.

As you can see in the demo, OpenCode runs entirely in its own cloud sandbox. Compilers, runtimes, dependencies, and other tooling are already installed and ready to use.

So there’s no:
“Run the agent on your PC and control it from your phone.”

The agent actually runs in the cloud.
Right now, the beta includes free Zen models, generous usage limits, and no paid subscription.
I’m currently looking for more beta testers, and a mobile version of Syntropy Beta is coming soon as well.

If you’d like to try it, leave a comment and I’ll send you an invite.

Feedback is very welcome - especially criticism.

Just enjoy.
But I’m still eagerly waiting for vision support. Once it arrives, this model will be something truly incredible.
For me, that feature is essential. Without it, my hands are tied.

GitHub suddenly signed me out of my account and sent me an email saying that suspicious activity had been detected and my password was forcibly reset.

The problem is simple: I cannot reset it.

What happens:

1. I open the official GitHub password reset page.
2. I enter the verified email address linked to my account.
3. GitHub sends me back to the sign-in page.
4. The URL contains a redirect to /suspended.
5. My old password no longer works, but GitHub never gives me a working way to create a new one.

Since then, I have received the same password reset notification several times. Every new attempt ends the same way.

I have already opened a support ticket and provided the account ownership information they requested. I still do not know whether someone actually accessed my account, whether this was a false positive, or when I will be able to sign in again.

Has anyone here experienced the same recovery loop?

How long did GitHub Support take to restore your access?

I am not asking Reddit users to recover the account for me. I just want to know whether this is a known problem and whether anyone from GitHub can explain why the security system forces a password reset while simultaneously preventing the user from completing it.

Today I jumped in and tested Flash 0731, and the first thing I said was: “This is a fucking different model. It behaves like a grown-up GPT-5.6 Sol!”

So I tested it for a while, then went to check the benchmarks. And obviously, I wasn’t surprised to see Flash 0731 beating Fable 5 in a bunch of them.

But holy shit, I’m still shaking when I realize that 123 million tokens - yes, obviously with 98%+ cache hits - cost me only 0.98$!!!

These last three days have been absolutely insane. Kimi K3 shows up on Hugging Face, Luna gets cheaper, and DeepSeek drops a killer.

All I can say is: enjoy it! This is an amazing time, and right now, we as consumers are clearly winning.

If DeepSeek keeps pulling off miracles like this, I’m buying three liters of lube, finding the head of DeepSeek, and spending a whole week tickling his balls while keeping them generously lubricated))))

Many of my readers recently pointed out something fundamental.

It does not really matter how much a model costs.
What matters is how much it costs to complete a single task.

And this is where we can finally see the real picture.
It is a shame DeepSeek is not included in this test.

I found the data on X and slightly redesigned the visual to make everything clearer and easier to understand.

Well, I’ve already had the chance to test the new DeepSeek V4 Flash 0731 in OpenCode.

And I noticed the difference from the very first task.
This felt like a completely different level of model. It wasn’t just the final result that changed - the entire approach changed: planning, logic, sequencing, and overall understanding of the task.

At one point, it strongly reminded me of how GPT-5.6 SOL works. And honestly, that says a lot.

Then I decided to give it a very small prompt: create a lightweight YouTube clone.

The task was extremely simple, but even when building a basic website, I could clearly feel the improvement. The model seemed much better at understanding structure, visual logic, and what the final result should look like.

This is not just a slightly improved version of the old Flash.

It feels like a completely different level and a completely different way of working.

Keep it up, DeepSeek! You’re the best ❤️

Who has already tested Flash 0731? Share your impressions 🙌

It looks like my post about Flash being ready to take any hit became outdated before I even published it.
Because now Flash is not just ready to take hits - it has gone on the offensive 🥊

DeepSeek has just updated the model to DeepSeek-V4-Flash-0731, and the performance gains are absolutely massive.

The architecture and model size remain unchanged. Instead, DeepSeek completely redid the model’s post-training, with a particular focus on coding, agentic tasks, and tool use.

The new version is already available through the API under the same model name: deepseek-v4-flash

Flash has also received native Responses API support and a dedicated adaptation for working with Codex.

Flash did not simply become slightly better.
In some benchmarks, its performance increased several times over.

It significantly outperformed its own Preview version, crushed V4 Pro Preview in agentic and coding tasks, and in some tests reached - or even surpassed - Claude Opus 4.8.

And all of this was achieved not by DeepSeek’s flagship model, but by the small and inexpensive Flash model with just 13 billion active parameters.

Remember, not long ago we were discussing Luna’s price cuts and how strong it had become in terms of price-to-performance.

It looks like Luna did not get to enjoy the top spot for very long 😅

This is exactly what I was talking about before: we do not use Flash simply because it is cheap.

This is not a compromise where we say: “Well, the model costs almost nothing, so we can tolerate the lower quality”
No.
Flash is incredibly powerful and intelligent first. Being cheap comes second.

There is also one important detail: only Flash in the API has been updated so far. V4 Pro, the DeepSeek app, and the web version have not received this update yet. The updated V4 Pro is expected to arrive later.

And if the new Flash is already producing results like these, I am honestly afraid to imagine what DeepSeek is preparing for the full V4 Pro release.

Chinese companies genuinely deserve our thanks. They are increasing competition, lowering prices, and forcing the entire industry to move faster 👏

And to the American companies, we have only one thing to say: Get ready. It’s going to get hot 😈

Do you think this is the best model update of 2026, or is it still too early to draw conclusions?

Flash is still the best ❤️
https://pico-pu-calculator.pages.dev

OpenAI has just reduced API prices for GPT-5.6 Luna by 80% and GPT-5.6 Terra by 20%.

The new standard API prices per 1 million tokens are:
GPT-5.6 Luna
Input: $0.20
Cached input: $0.02
Output: $1.20

GPT-5.6 Terra
Input: $2.00
Cached input: $0.20
Output: $9.00

AI Model Price Comparison & Cost Calculator:

Simply enter your input and output tokens to instantly calculate the cost of a request for any AI model.

🛑🛑https://pico-pu-calculator.pages.dev🛑🛑

For shorter-context requests, Luna can be even cheaper at $0.10 input and $0.60 output per million tokens.

According to the Artificial Analysis chart shared by OpenAI, Luna now delivers one of the strongest intelligence-per-dollar ratios on the market. At these prices, it looks extremely competitive against DeepSeek, Gemini, GLM, and Claude.

Website: https://pico-pu-calculator.pages.dev

I got tired of constantly guessing how much each request would cost across different AI models.

I wanted a simple website where I could paste a prompt and its response, select several models, and calculate the actual token usage and estimated cost.

So I decided to build it myself.
GPT-5.6 Sol helped me a lot by gathering a huge amount of information and designing a genuinely solid architecture for the project.

The documentation covering the architecture and internal mechanisms alone ended up being 52 pages long 🫠

The website lets you compare the cost of the same request across multiple AI models at once, so you can quickly see which model is the most cost-effective for your specific text.

Everything is completely free, works directly in your browser, and requires no registration.

I’d really appreciate it if you tested the website. If you notice any bugs, calculation errors, or other issues, please let me know 🙂 I’ll do my best to fix everything.

I’d also be happy to hear any ideas or suggestions for improving the website ❤️

Both models received exactly the same prompt. Each result was generated on the first attempt-without any follow-up instructions, corrections, or refinements.

Here was the prompt:
>!Create a landing page for selling cryptocurrency courses. The website should look beautiful and incorporate modern design and animation trends. It should feel truly alive. I expect finished, working code.!<

In the end, DeepSeek produced an incredibly basic website. I’ll go even further: websites like this are starting to genuinely annoy me.

DeepSeek keeps generating designs that look as though they were copied from the same template. Its work is so repetitive that I can recognize a DeepSeek-generated website at a glance.

DeepSeek also said nothing about responsiveness or adapting the site to different devices. For comparison, GPT-5.6 SOL not only generated the code but also gave me a detailed breakdown of the project architecture, component structure, and implemented technical decisions.

My verdict: when it comes to modern web design, DeepSeek has been left far behind. In the past, its approach to development might have made me smile. Today, it causes frustration and genuine irritation.

The model repeatedly reproduces the same outdated patterns and shows almost no visual initiative. In 2026, this level of front-end and interface design feels especially unacceptable. A product performing at this level simply should not be competitive in today’s market.

P.S. This criticism does not apply to the DeepSeek API as a whole. DeepSeek performs quite well in agentic tasks that are unrelated to design, especially in basic and moderately complex scenarios.

For regular agentic work, I personally use DeepSeek almost exclusively and am very satisfied with it. But when it comes to building modern interfaces and handling the visual side of web development, the gap is simply too obvious to ignore.

I was comparing the results of different models on agentic benchmarks because I wanted to see which models DeepSeek V4 could realistically be compared to.

This is roughly what I found:
GPT-5.4 Mini XHIGH ≈ DeepSeek V4 Flash Max
GPT-5.6 Luna Medium ≈ DeepSeek V4 Flash
Sonnet 5 High without thinking ≈ DeepSeek V4 Pro

At the same time, DeepSeek V4 Flash outperformed GPT-5.6 Luna Medium in agentic tasks. In fact, DeepSeek scored better in three of the evaluations.

I found this benchmark to be high-quality and reliable. Honestly, I’m not surprised by the results. I’ve been using DeepSeek for a while, and in practice it really does perform at a very high level.

I’m very happy with DeepSeek. Considering its price and capabilities, the result is especially impressive.

I wanted to turn the regular DeepSeek web chat into something more than just an answer generator.

Right now, when you ask an AI to build a program or a website, it usually gives you the code, but you still have to run it, test it, and fix any errors yourself. I’m building a browser extension that tries to automate that process.

The user enables a single button and describes the task in a normal message. The extension then guides DeepSeek through creating a plan, building the project, running it, checking the result, and attempting to fix any errors it finds.

Ideally, the user shouldn’t have to see all the technical details happening behind the scenes-only clear progress updates and the finished result.

This is still a very early alpha, so the design, stability, and output quality are naturally quite rough.

Right now, I’m mainly testing the concept and gradually putting together a reliable workflow.

I just wanted to share what I’m working on. Does this kind of mode seem useful to you, and what kinds of tasks would you try it on?

I decided to run a test.

The result:
Purely technical performance: 1:1 (in my opinion)
Visual/design quality: 1:0 for CODEX
Execution time: OpenCode - almost 7 minutes. CODEX - 16 minutes. Point goes to OpenCode.
Cost: OpenCode - $0 (55k tokens used). CODEX - 2% of my weekly limit (Plus subscription).

Winner: OpenCode DeepSeek.

Yes, CODEX really does produce better designs. But nobody stops you from using Figma for free. Figma can create a beautiful working mockup, and then you can give that design to OpenCode.

Is this a workaround? Yes.

But damn… the price is $0 for this task.

And honestly, even GPT-5.6 SOL High inside CODEX failed to create a proper design in one of my projects.

I provided screenshots, parts of the code, GitHub descriptions, and a lot of additional context - but the result still wasn’t good enough.

Who won? I’ll let you decide.

But look at the numbers:

2% of my weekly CODEX limit for 16 minutes of work.
Sometimes CODEX can consume even more in just 6 minutes depending on the task.
Based on my test results, CODEX uses around 7–9% of the weekly limit per hour.

That means the maximum you can realistically get from the weekly quota is around 11–14 hours of continuous agent work.

Or roughly:

around 2 hours per day in a perfect scenario
closer to 1.5 hours per day in a worse scenario
I’m curious to hear your thoughts 💭

Who would you choose: paid CODEX or free DeepSeek through OpenCode?

So, I’ve finally had a serious real-world work session with both CODEX and OpenCode. Here’s what happened.

I used the DeepSeek V4 Pro model in both agents.
As I said in my previous post, token consumption in CODEX is several times higher. I complained about it.

But after a very serious work session, I can confidently say that the CODEX agent is the best thing I have ever encountered in my life.

OpenCode is not bad, but it constantly forgets parts of the tasks from the technical specification. It constantly lies to me.

It says that something has been done, but in reality, nothing was done-or even worse, it simply created placeholders. It also often could not solve a problem, even one that was not particularly difficult. Even on the third attempt, it still failed to solve anything.

CODEX + DeepSeek immediately found the problem and fixed it. It even explained the problem properly-OpenCode could not even do that.

I believe that CODEX fully justifies its increased token consumption. Its agent capabilities are on a completely different level compared with OpenCode / Cline.

When I work with CODEX, I feel like the task is being handled by a professional. Unfortunately, I do not get that feeling when working with OpenCode.

OpenCode consumes several times fewer tokens. But because of the constant problems, you may ultimately spend even more tokens constantly reworking the project.

It will also take more time.

OpenCode is an excellent and economical option for small projects. But when you are building something serious-CODEX only.

P.S. The proxy that allows other models to be added to CODEX is not my development.

I found it on GitHub.
I am against any violations of the law or OpenAI’s rules, and I completely condemn the author of this patch / proxy that allows other models to be added. Even though it is not a hack or a modification of the CODEX application, I still condemn it!

Here’s what happened.

The day before yesterday, I completely burned through my weekly Codex limit in about five hours: roughly three hours with SOL Max and another two hours with LUNA Max. I’m on the Plus plan.

Now here’s the really interesting part.

Today I found a patch that lets you add DeepSeek to Codex Desktop. You can see it in the screenshot.

I gave it one relatively small task using DeepSeek V4 Pro.

The result:
16 minutes of work
about $0.25 in API usage

Then I opened OpenCode, selected the exact same model, and gave it the exact same prompt.

The result:
2 minutes of work
about $0.02 in API usage

That’s roughly 12x cheaper.

As for the quality: since both were using the same model, the results were pretty similar. Both delivered a working app.

Codex using DeepSeek did a slightly better job on the terminal app’s design, but the difference was tiny - maybe around 5%.

Honestly, I’m kind of shocked by the gap.

Drop some interesting prompts in the comments. I’ll run another comparison, record the whole screen, and show the API costs for both Codex and OpenCode.

I think this deserves a much deeper look.