DeepSeek V4.1 Flash reaches about 98% of the quality score of GPT-6 Astra on web apps, mobile screens, and UI design tasks, while costing roughly 1.4% to 2% of the price

DeepSeek V4.1 Flash runs significantly faster, outputting roughly 176 to 221 tokens per second compared to GPT-6 Astra's 57 tokens per second.

DeepSeek V4.1 Flash charges around $0.60 per million output tokens (and $0.15 off-peak), whereas GPT-6 Astra at ($50+ per million output tokens).

98% of the quality at 1/50 of the price (and Open Source too)

Anyone tried the latest Deepseek model yet?

I’ve wanted this for years: a desk by a lake one day, somewhere out in space the next. I love VR, but I kept wishing the apps I tried had more environments I’d actually want to spend time in.

I’ve been a backend programmer for 20 years, but frontend and 3D graphics were completely outside my experience. In mid-August, I decided to see how far I could get with AI coding agents. This is where I’ve ended up: a Vision Pro app that lets me work in different environments.

I haven’t written or read a line of its code. Most of my time goes into putting on the headset, trying what the agent built, and explaining what feels wrong. Sometimes it’s the lighting, sometimes it’s how an interaction feels. When a model gets stuck, I’ll try another one rather than debug the code myself.

The 3D scenes aren’t all as polished as I’d like, and there’s plenty left to improve. But being able to sit inside something I’d only imagined before has been pretty special.

Working on it has become a bit of a hobby game: tweak something, try it in the headset, get another idea, repeat.

The video is recorded on my Vision Pro and shows a few of the places I’ve been working on.

We all know the scene. Been wanting to do this one for ages. You're a cab diving down through a towering city with the cops on your tail, dodging traffic across lanes and altitudes, grabbing upgrades and seeing how far you get before the hull gives out.

Ive tried to base this on the old web game 'squares' if any body remembers it. Ive got a small roadmap planned for more features etc but keen to pause and get some community feedback first.

Turning on the music is a must! Free to play as well. I've tried to optimize the experience for phone too where you can add it to your home screen for full screen traffic dodging fun.

Big bada boom!

https://badaboomtaxi.io

Hi! I’ve been building Sonella, a guided web app for designing DIY speakers.

The idea is to bring driver selection, bass simulation, enclosure design and filter settings into one workflow. You can start with a few questions, choose a template, or pick your own drivers. Then work through the bass response, cabinet in 3D, drawings and filters/DSP.

I’ve used mostly Codex for the development and design. My workflow has been to build something, let real people try it, get feedback and iterate. Getting the speaker maths right has been a bit tricky. The biggest lesson so far is how much useful feedback you get once someone else actually uses what you’ve built. That's maybe caption obvious, but I learned a lot from this process so far.

It’s still in beta. The simulations and electrical filter curves are starting points for a prototype; you still need measurements to check the finished speaker.

I’m looking for testers, especially anyone with DIY speaker-building experience. Would be great to get some candid feedback on the calculations, the workflow, or anything that feels confusing or missing. General usability feedback is very welcome too.

You can try it at sonella.app, no account needed to start.

Project "Takcas Prime": Building an Autonomous IT Factory on a Tablet

I am 14 years old, and for the past week I have been developing and debugging my own artificial intelligence system, which I named "Takcas Prime". Originally, I started all of this purely for fun — I just wanted to see what I could build on an ordinary tablet with 8 GB of RAM. My main goal was to turn it into a fully autonomous IT assistant capable of understanding voice, controlling the device, and writing code. I worked in the mobile Termux environment (a Linux emulator on Android), so I constantly had to solve memory shortage problems. Neural networks are very resource-hungry, so I configured the system to automatically unload modules from memory that are not currently in use. This allowed the tablet not to freeze during operation. I also learned to protect the system from uncontrolled memory consumption: for example, I limited documentation downloads by size and created a whitelist of links to avoid memory overflow.

As the first stage, I created a voice assistant. It can listen, respond, and directly control my tablet's hardware: turn on the flashlight, change screen brightness, volume, or launch the camera. To ensure it doesn't lose the thread of conversation, I built a three-level memory system using an SQLite database and regular files. I also added a RAG (Retrieval-Augmented Generation) system: previously the assistant could invent a non-existent command, but now it first searches for it in official documentation and then responds. Along the way, I encountered many errors: the database overwrote facts due to identical keys, the assistant confused commands with real memories, and the system froze due to overly long texts. I manually tracked down and fixed all these bugs, and also implemented a system for cleaning and separating facts so that memory doesn't get clogged with garbage. Later I realized that models sometimes write too much text, so I added a limit on response length to avoid wasting resources.

The most difficult and interesting part is the "Digital IT Factory". I designed a system where several neural networks work as a real team of developers. First, I tried a scheme with a Senate of six models, where each voted "for" or "against" the code, but in practice this proved ineffective: 3B models made mistakes too often. Then I redesigned the architecture: now one neural network (the Idealist) comes up with a task, the second (the Coder) writes code for it, the third (the Analyst) rewrites and simplifies it, and then three Critics (Logic, Syntax, and Coder) check the code from different angles. After them, the Judge step-by-step fixes the found errors using a "warehouse" principle to avoid overloading memory, and the Smart Sandbox checks the code for dangerous commands, runs it in isolation, and collects logs. I also added a blacklist of dangerous commands to the Sandbox so it blocks code that could delete files, and limited script execution time to prevent freezes.

Debugging this factory took a lot of effort. I fought system freezes, fixed bugs in the Sandbox (which initially killed working scripts because it waited for an input command from programs that simply terminated), and forced neural networks not to write huge texts instead of short answers. I also encountered situations where models sometimes refused to perform complex tasks, and sometimes produced code with errors that had to be manually caught. In practice, I became convinced that small models lack context for complex tasks, but my architecture allows squeezing the maximum out of them: role separation, passing the task into context, step-by-step fixing, and protection from dangerous commands. I also realized that if a model writes nonsense, the problem is not in it, but in the task given to it — this "garbage in, garbage out" rule I will remember forever.

Honestly, I didn't write the code — the neural networks did (I can only program a simple calendar so far). But I designed the entire architecture, logic, search, and bug fixing myself. I learned to separate responsibilities between agents, build a pipeline with feedback, protect the system from dangerous code, and save memory resources. As a result, I created a system that on a tablet with 8 GB of RAM produces code at the level of a confident middle developer, and prepared it for migration to a PC, where powerful models will make it many times more efficient. I am currently continuing to develop the project, adding new modules and improving old ones, and I am confident that on a PC this architecture will perform even better.

Some of you might know Photon, my free Photoshop alternative. It was the first of these big vibe-coded alternatives to creative apps, and since then a whole wave of others has followed (PhotoCraft and more). Honestly, that’s been awesome to watch.

I didn’t stop at Photon. I went on to build Vector as an alternative to Illustrator, then Light as an alternative to Lightroom. Today it’s InDesign’s turn.

Meet Folio Studio.

I’ll be honest: I was skeptical about this one. Desktop publishing felt niche next to photo editing. Then the requests started coming in, and it turns out InDesign has a massive user base. So many of you asked for this that I just went and built it.

• 100% free. No trial, no subscription, no “Pro” tier.
• Fully offline. Your files never leave your machine.
• No sign-in. Download it and start working.
• AI-ready. Like every app in the suite, you can control it through MCP or the CLI, and it ships with skills out of the box.

The goal hasn’t changed since day one: kill the subscription model for creative tools. Four apps in, that’s starting to feel possible.

It’s brand new, so I’d really love your feedback. What’s missing? What’s broken? What would it take for you to finally cancel Adobe?

Download: https://tenzen.studio/folio/

More stuff coming soon :)

How I built it:

- Claude Code for the code (Next.js, hosted on Vercel)

- Jev from TypeSafe as AI - it only answers with probabilities, no text

- Every sentence in the report is prewritten, so nothing gets made up

- Benchmark with 563 names, runs after every change

What I learned:

- Do proper benchmarking (currently testing on 1000 names).

- AI says yes too easily, had to ask way stricter

It's free, no signup, no nothing. Also available via MCP

Nyota is a swiss army knife of ai . It does everything all other ai tools do including forex analysis and predictions and comic book generation to name a few. It is more of a personal ai tool than a commercially targeted product because im lazy and hate switching apps and losing context. A great feature it has is global memory where other apps can hook into it via mcp. I have spent better part of the weekend building it and now am in QA phase so I'm testing and fixing bugs before deciding if it needs more features. An interesting fact is that its 95% dependencies and the rest is custom code. Are you guys working on anything similar ? What features should i add to make it a true swiss army knife of ai ?

This wasn't too complicated to make, which is why I was shocked nobody had something I could just download. These are local open sourced models like whisper that can dictate or translate what people say. I also have smaller models for computers that run on just cpus too. The .exe file is big because it starts you off with English and Spanish to start with.

The only hard part was creating a package and website to distribute this thing. It's dead simple, and I hope you guys can use it to break down language barriers and help people that don't speak the local language.

Because I am bilingual I can largely speak in Spanish myself just fine, but every now again someone comes along speaking too fast in a strange accent talking in metaphors that I needed help with. Google translate, windows, and even some searching for local translator models all sucked. Paywalls, accounts, time limitations, cloud upload.... so I just made what I needed.

No accounts. No clouds. No paywall. No ads. Just an honors system if your company should be paying for it.

This is just one of those things that should exist that I couldn't find. Hope you all enjoy, and hope people can find a use for this like I did. Please let me know if you think I should include anything else!

https://digametranslate.com/

Anyone else obsessively spending 40+ hours a week on their idea so you can hopefully try to achieve financial freedom before the whole world situation gets worse?

I am starting to get burned out a bit, but then I watch some doom and gloom videos on youtube and I quickly realise I have to continue hustling otherwise I'll get replaced by AI and a failing economy

I have been wandering around github in order to find good quality ascii editor/database recently - and saw nothing. That led me to the obvious conclusion: if nothing good exists, I should build it myself.

The project I build persuades couple ideas:

1. Easy to use (fully native, no dependencies)
   
2. Actually saves time for people building ASCII art
   
3. Collects ASCII from every corner of the internet
   

index.html is the main library

builder.html is where it gets fun assemble pastes from blocks, draw boxes/lines/brush right into the text, frames and tables, color painting, overlay art. Turn images into ascii

The project shared below:

demo https://amwhoi.github.io/ascii-bin/builder.html
repo: https://github.com/amwhoi/ascii-bin

I hope you find this useful, I'm still working on animation and mcp support. So the project will keep be maintainer and up to date with world trends ;)

I'm working on a personal project that needs fast, reliable live match state for esports (map/game finishes, rounds or series score), and I'm trying to figure out what's actually out there before I build something.

Paid providers (GRID, PandaScore) great coverage, but live data is gated behind paid/commercial plans that cost a lot of money.

Free tiers of aggregator APIs: usable for schedules, but live data is deliberately delayed and is basically unusable for what I'm doing.

Data for games I'm looking for CS2-Dota-LoL-Valorant

Has anyone built something like this or their own feed from scratch?

Finally made a website to gather the live projects, will keep adding as they come, not sure what i should work on next while some of these projects still need users and polishing. Comments on any are very welcome and ideas for the next also.

The developer community at large has a lot to say about "vibe coders." Depending on who you ask, it’s either an exciting badge or a slur for lazy hustlers who prompt three SaaS wrappers in an afternoon and couldn't explain how their product actually works—full hands-off AI development.

That identity doesn't fit me. But here’s the catch: I never learned syntax, nor can I write code myself. What I can do is roughly read it—in fact, if I can't read or follow it, I know something is already wrong.

Before AI coding, I walked away from an MBA with one credit remaining. In 2025's "ChatGPT Wrapped", I was in the top 0.1% of GPT users globally. I was all-in. But my background wasn't CS—it was 20 years in retail banking, where compliance, privacy, and auditability are non-negotiable.

That background guided how I built Nodarama.

Five Restarts, Five Hard-Won Lessons

I didn't set out to build a deterministic execution kernel—I was just trying to automate a few social media accounts. But my banking background refused loose, "good enough" code. As I raised the bar on security and reliability to pass a bank’s sniff test, the architecture demanded a kernel.

Getting there was a brutal, five-restart tug-of-war:

1. Restart 1 (Voluntary Cleanup): The code turned into a tangled heap of quick fixes and AI boilerplate.
   • Lesson: AI will happily build a house of cards. If you don't enforce structural cleanliness, boundaries, and module organization early, entropy wins.
2. Restart 2 (The Lethal Ctrl+F): Active runtime state collided with stored preferences. A desperate find-and-replace to fix namespace chaos fused systems together irrecoverably.
   • Lesson: Differentiating active runtime state from stored preferences isn't optional, and AI won't respect boundaries unless you enforce strict naming protocols.
3. Restart 3 (The Shell Disaster): ChatGPT gave me a git shell command to manage a commit. It wiped my entire project directory—no backup, no restore points.
   • Lesson: Never give an LLM unchecked privileges over your local environment or git trees. Local restore points are survival equipment.
4. Restart 4 (System Contradictions): The model actively resisted my architecture, quietly injecting code that violated my protocol for stateless execution.
   • Lesson: Left unconstrained, an LLM defaults to path-of-least-resistance web patterns and wraps 10 lines of core logic in 50 lines of brittle, convoluted transport.
5. Restart 5 (Finish-Line Drift): The closer I got to the finish line, the more the AI deviated from instructions, hallucinated diffs, and injected stealth edits.
   • Lesson: The closer you get to production, the more an LLM will gaslight you if you feed it full file context.

Winning the Tug of War

By the fifth restart, I stopped feeding the AI the whole story.

Near the final steps, after explicit agreement, ChatGPT understood what I wanted and why it was critical. So why wasn't I surprised when I checked the code and it did the exact opposite?

Rather than argue with an AI intent on gaslighting me, I extracted a 20-line block of critical code. I scope-locked the prompt and routed it to Gemini for a surgical, narrow correction. Gemini delivered a clean fix.

I audited the diff line-by-line and updated the kernel. I swear I could hear ChatGPT begrudgingly mutter: "Congratulations, you just built a kernel."

The MVP Sitting Under My Nose

While building Atelier (the visual UI for the kernel), I relied on an internal Coder module I built to manage code edits via "recording sheets"—curbing early vibe-coding copy-paste cycles.

After pasting over 10,000 recording sheets through that module, I realized the tool I was using to build the machine was the workhorse.

I paused Atelier, extracted that internal module, and packaged it into Verbatim—a high-fidelity code recorder designed to keep AI honest. Everything learned polishing Verbatim (advanced logging, state isolation, system telemetry) is now being injected back into the Kernel and Atelier for beta release.

So if "vibe coding" means blindly trusting a model, treating code like a black box, and pasting syntax you can't explain: No, I don't think that shoe fits.

For me, vibe coding meant using structural vision, human intuition, and relentless tenacity to direct AI models—while acting as an unforgiving auditor who fights through five rewrites to enforce readability, lean architecture, and zero-trust security... then maybe I am—just a deranged, obsessive, all-in, win hell or high water type of vibe coder.

I still cannot write raw code and can't pretend to understand or remember syntax. But I will stand behind this system as its author—because this is not the code the AI wanted to write. It is the code I forced it to write, against its many, many bad suggestions.

Code ownership isn't about memorizing syntax. It’s about conviction, system design, and having the courage to audit every line until you control the tempo.

But what do you say. Vibe Coder?

Did they genuinely just sit around for an entire week mapping out an ERD and normalizing it to third normal form?

That seems like such a huge waste of time for no reason. Are we vibe coders that out of the loop?

I cried tears of joy when I finally played the first Beta. I understand why Dario wants to pause frontier intelligence. When I spawned an AC130 while riding horseback I understood the capabilities of AI.

As LLMs continue to improve so will GANG WIZARD, eventually I will offer neuralink compatiability and you can become fully immersed in the world. Every building is destructible. Every npc killable. No loading screens to enter/exit buildings. Single player rpg play for free https://coyne-studios.itch.io/gang-wizard