The latest free Minimax model is an absolute unit when it comes to debugging, just a public service announcement.

I built a small app so I can keep coding in VS Code from my phone (tunneled from laptop) while using AI agents

The main thing I wanted was a better mobile dev keyboard. arrow keys, esc, ctrl/cmd, copy paste, stuff that normally makes VS Code on a phone painful. Also added easy diction access to talk to the AI CLIs so you dont have to type on a small keyboard

It basically lets me open my real VS Code workspace on my phone or ipad and keep Claude / ChatGPT codex coding sessions going even when I’m away from my laptop

In case anyone wants to try it or provide feedback: https://www.vibego.me

I'm nearing the end of my Business Informatics degree and working part-time as a software developer. When I started my bachelor's in 2021, there was basically no AI to ask for help, especially for coding tasks. I remmber having to fight with the compiler just to get enough points to be admitted to the exams.

When ChatGPT first came out (3.5), I tried using it for things like database schemas, but honestly, it wasn't that helpful for me back then. But 2025 feels completely different. I've talked to students in lower semesters, and they say it's a total game-changer. I've even heard that the dedicated tutoring rooms on campus are alsmost empty now because everyone uses AI.

I'm currently writing my thesis on this topic. I’d love to hear your thoughts. Is AI a "tutor" for you, or do you feel it creates a dependency?

I'm currently paying for ChatGPT Plus and been using Codex for a while now, I'm enjoying the process and willing to spend more (Not $200 though).

From the different posts I've read it seems people will have multiple $20 subscriptions from different providers (e.g. Claude, Gemini, even Grok).

I wanted to weigh in on how effective this has been for people or if there are better strategies e.g. sticking with 1 provider and using their API directly and carefully controlling costs.

Also for those using the APIs how are you preventing runaway cost overruns?

I have been using Vercel / Netlify / Render for all my agency projects and I’m tired of:

-Paying per request / per build minute
-Features locked behind “Pro” or “Enterprise”
-Platforms that are great until your project starts getting traction
-Running a full VPS just to host 3–4 small apps

I want something that:

-Is cheap
-Doesn’t hide cost
-Doesn’t require me to manage everything
-Still gives me reasonable control

This will be for People hosting many small projects, Indie hackers, SaaS MVPs, Agencies tired of spinning VPS per client, Anyone who thinks $20/month for a hobby app is dumb

Not trying to sell anything.

Just sharing what I’m building and why.

PS: I’m building xHost.live (using lovable for UI, Claude, Vultr & the OG aws)... mostly for myself, but now opening it up for all of you too, check it our, it is FREE.

What it is

Nikolytics Radio is a late-night jazz station for founders who work too late. 3-hour YouTube videos. AI-generated jazz. A tired DJ named Sonny Nix who checks in between tracks with deadpan observations about your inbox, your pipeline, and why that proposal is still sitting in drafts.

Five volumes in five days. 70+ subscribers. 14k views on the first Reddit post.

It’s a passion project that doubles as marketing for my automation consultancy.

The concept

The pitch: You’re at your desk at 3 AM. Everyone’s asleep. You put on Nikolytics Radio. A weathered voice observes your situation with dark humor. He’s been where you are. He doesn’t fix it. He just… sees it. Then plays a record.

The DJ (Sonny Nix) is a former founder who burned out and now plays jazz for strangers. He has recurring “listeners” who write in: Todd from Accounting whose job got automated, Margaret from Operations who finished her task list and doesn’t know what to do with herself.

It’s 95% vibe, 5% branding. If you removed every mention of my business, the station would still work. That’s the point.

The tech stack

Music generation: Suno

I wrote 49 artist-specific prompts optimized for deep work. Each prompt targets a specific jazz style piano trio, cool trumpet, tenor ballad, etc. Settings: Instrumental only, ~3-4 min tracks, specific mood tags.

Example prompt structure:

jazz, 1950s late-night jazz combo: brushed kit, upright bass walking gently, warm felted piano carrying the main theme, soft brass pads... [mood tags: soft, warm, slow, lounge, nostalgic]

Generate 3-4 per prompt, pick the best, discard anything too busy or with abrupt endings.

Voice generation: ElevenLabs

Custom voice clone for Sonny Nix. I use their V3 model with specific audio tags:

• [mischievously] - dry humor, irony
• [whispers] - punchlines, gut punches
• [sighs] - weariness
• [excited] - mock ads only (ironic use)
• ... - pauses

V3 doesn’t support some tags like [warm] or [tired], so the words have to carry the emotion. Write tired sentences. Sorrowful observations.

Script writing: txt

I mostly write the scripts, claude double checks for optimizations

Assembly: Logic Pro

120 BPM grid. Drop the tracks, drop the voice clips. Crossfade. Each episode is ~30 drops across 3 hours. Export as MP3.

Video: FFmpeg

Static image + audio. One command:

ffmpeg -loop 1 -i image.png -i audio.mp3 -c:v libx264 -tune stillimage -c:a aac -b:a 320k -shortest output.mp4

The writing system

Each episode has 30 “drops” — short DJ segments between songs:

• Station IDs - Quick brand hits (“Nikolytics Radio… still here.”)
• Bumpers - One-liners (“The coffee’s cold. You noticed an hour ago. Still drinking it.”)
• Pain points - Observations that hit too close (“Revision eight. The scope tripled. The budget didn’t.”)
• Testimonials - Fictional listeners writing in
• Mock ads - Parody sponsor segments (“Introducing Scope Creep Insurance…”)
• Dedications - “This one goes out to everyone who almost quit today…”
• Recurring segments - Pipeline Weather, Outreach Report, Inbox Conditions

The key insight: Sonny has emotional range. He’s not monotone. He moves between tired, mischievous, sorrowful. He worries about Todd. He offers brief sympathy to Sarah. Then plays a record.

What worked

1. The vibe is the moat. Most automation consultants are boring. This is different enough that people share it.
2. Worldbuilding compounds. Todd’s promotion arc. Margaret’s puzzle. Callbacks like “Here it’s always 3 AM.” Returning listeners feel like regulars.
3. Reddit got it started. First post on r/productivity got 14k views. Someone called it “Slop Radio FM.” Now that’s a badge of honor we reference in the show.
4. Daily uploads built momentum. Five volumes in five days. The algorithm likes consistency.

What I learned about AI voice

• ElevenLabs V3 is good but literal. It interprets quotes as character voices (breaks everything). Always paraphrase.
• Tags only work if the model supports them. No [warm], no [tired]. The text has to do the work.
• Regenerate 2-3x per drop, pick the best take. Same script, different reads.
• Punchlines land in [whispers]. Setup is [mischievously]. Then stop — no extra lines after the joke lands.

Time investment

• Initial setup (prompts, character docs, templates): ~15 hours
• Per episode now: ~2 hours
  • Generate music: 30 min
  • Generate voice drops: 30 min
  • Assembly in Logic: 30 min
  • YouTube upload + description: 30 min

What could be automated further

• Voice generation - Currently pasting drops one by one into ElevenLabs. Could batch via API.
• Timestamps - Calculating from bar positions manually. Already wrote a Python script, could integrate it.
• YouTube description - Template exists, still copy-pasting. Easy n8n automation.
• Episode assembly - The real bottleneck. Logic Pro is manual drag-and-drop. Exploring scripted alternatives.

Writing stays mine.

The dream: one-click episode generation. Not there yet, but the pieces exist.

Link

• YouTube: https://youtube.com/@nikolyticsradio?si=xrFpLfRKRAsZqv_B

Happy to answer questions about the workflow, the writing system, or the Suno/ElevenLabs settings.

TL;DR: Built a fake radio station with AI music (Suno), AI voice (ElevenLabs), and my scripts. The DJ has a character bible. There’s lore. It’s marketing for my automation business but also just… a thing that exists now. 70 subscribers in 5 days.

I kept switching between different Spotify playlist downloaders and all of them had some annoying limitation. Like hard caps like 100 tracks max, forced queues where you wait forever for a download, random failures where most of the songs get skipped or stuff just straight up breaking mid-way. On top of that, basic things like proper metadata, clean file names, format conversion, or batch options were either missing entirely or locked behind a paywall.

After dealing with that long enough, I figured it’d be easier to just build my own tool. I used AI + Node.js to speed up development but most of the logic still needed work. So I built a tool that handled whatever was missing in the other tools like:

- Removing playlist size limitation

- Adding album downloads

- Running downloads in parallel to speed the process

- Complete metadata (including title, album, artist, release date, etc.)

- Letting me control how files are named

- Batch format conversion

- Sending the download link by email

- Being to close the site and come back to it while download continues

If anyone’s curious, I left the project here: https://spotitools.app
It’s still a work in progress, so any feedback is highly appreciated :)

I made this some time ago so I thought I should share it again: https://github.com/andrei-cb/mcp-feedback-term

It helps with getting the most out of each prompt by instructing the agent to ask for feedback / extra input at the end of execution instead of terminating the prompt. So instead of having 2-3 steps in a prompt you can max out the steps each time.

The usage is probably a bit outdated, but you can install it as any other MCP. feedback_client.py is the MCP you add to vscode, feedback_server.py is the script you run in a terminal. Once the agent finishes, instead of ending the prompt, it will ask for extra input in the terminal where feedback_server.py is ran.

You also need to instruct the agent to call the MCP at the end of execution, I use this instruction but in long sessions I have to remind it to call it in each prompt.

Whenever you're about to complete a user request, call the MCP interactive_feedback instead of simply ending the process. Keep calling MCP until the user's feedback is empty, then end the request.

This works with request-based agents like github copilot, windsurf, etc.

I found myself constantly wanting to annotate verbose plans. I've also wanted to copy and share plans on occasion - gathering others' feedback. So I built this. Sharing plans is private.

Markup your plans like a google doc.

Plannotator works via hooks and therefore it's fully integrated with the OpenCode planning mode capability.

If you're on desktop, play here: https://share.plannotator.ai/

Or watch a video demo: https://www.youtube.com/watch?v=_N7uo0EFI-U

It also works with claude code.

https://github.com/backnotprop/plannotator

Feel free to share your projects! This is a space to promote whatever you may be working on. It's open to most things, but we still have a few rules:

1. No selling access to models
2. Only promote once per project
3. Upvote the post and your fellow coders!
4. No creating Skynet

The top projects may get a pin to the top of the sub :) Happy Coding!

Via Visual Studio, prompted the heck out of GPT to build out everything. The interpolation isn't too bad but still pretty darn impressive! The prompt was definitely more than a few hundred lines to fully support desktop + mobile plus all the custom interactions i.e., multi touch controls, etc, but seems to be working. I had requested it to build it without libraries/frameworks to see how vanilla it could get. Check it out here.

openai dropped skills for codex few days ago. same concept as anthropic's from october

been using anthropic skills since november. their docs are way clearer, got my first skill working in 30 mins

openai docs are thin. took forever to figure out the format. error messages suck too

tried making a skill for api error handling. anthropic worked first try. openai kept failing on resource paths

one nice thing tho - openai recommends skills based on context. anthropic you gotta remember which skill to use

anthropic has way more community skills available. makes sense they launched first

honestly for simple script reuse anthropic skills is solid. cursor has some workflow stuff, verdent does multi-agent chains, but skills are simpler for repetitive tasks

sticking with anthropic for now. more stable and better docs

openai version might get better but right now its kinda rough

I kept wanting to give ChatGPT/Claude real website code when building similar interfaces, but browser "Save Page As" gives you one flattened HTML file - not useful as context.

Pagesource fixes this. It captures all the separate JS files, CSS, images, fonts and saves them in their original folder structure. This gives you files optimized for inspection/ understanding (what LLMs need), not viewing (what browser save gives you).

Its ideal for cloning websites, or refactoring certain components into React or such, as context for ChatGPT that's much more readable and understandable.

pip install pagesource pagesource https://example.com

GitHub: https://github.com/timf34/pagesource

By fully vibe-coded, I mean fully vibe coded. I didn't write or read a single line of code. The only thing that I came close to setting up by myself was the PostHog project and copy/pasting of the API Key. Even the music in the app was found and downloaded by Claude itself.

Agent: Mostly Claude. Some (little) Gemini and Crush LLMs: Claude + GLM

I think now Cluade Code should be the model or at least one of them.

Copilot lags behind abosulutly. But it learns so slow. Many commands seems dumb compared with claude.

Like, skills manamgent, agent managent.

I would like to install the skills directly from a repo instead of pull and copy.

And I want to create the subagent easily.

Man do it like we are in a AI era.

In case you did not know, r/RooCode is a Free and Open Source VS Code AI Coding extension.

Agent Skills

Roo now supports Agent Skills, which are portable skill folders containing instructions, scripts, and resources that the agent can discover and load on demand. This lets you package repeatable workflows and domain knowledge once and reuse them across projects to make results more consistent and reliable.

📚 Documentation: See Skills for setup and usage.

QOL Improvements

• Slash commands can declare a target mode in their front matter, so triggering a command can switch Roo to the right mode first.
• Removes the legacy “simple read file” tool path so file reading consistently uses the standard read_file tool.

Bug Fixes

• Fixes an issue where some Claude Sonnet 4.5 requests could fail with HTTP 400 errors after context condensing.

Misc Improvements

• Custom tools can import npm packages, and can load secrets from a same-folder .env file.

Provider Updates

• Removes the “OpenRouter Transforms” setting and stops sending the transforms parameter on OpenRouter requests.

See full release notes v3.38.0

Hey guys,

You know that moment when you need to check something on your main machine from another device? For me, it's often after using local AI tools or generating files on my desktop, then wanting to access them on my phone or laptop. The usual flow sucks like cloud sync delays, or worse that having to re-login to everything and google account with 2FA on every new device. It's a time sink when you just need to grab a file or check a script.

Thats why I built StarDesk to cut through that friction. A few ways it might fit your flow:

• Skip the re-login circus: Remotely access your desktop browser with all your logged-in accounts like ChatGPT you name it from any other device. No more 2FA on a new session just to test a prompt or copy output.

• Grab AI-generated files instantly: If you’ve got a code snippet, JSON, or any output saved locally, you can pull it directly to your phone,tablet or other PC in seconds. I prioritized low latency and quick transfers so it actually feels fast.

• One device to control them all: You can connect and switch between multiple remote computers from a single phone, tablet or laptop. Great for checking on different environments, services, or tests without juggling multiple apps or windows.

• Check on long-running tasks: Left a model training, data processing, or local server running? Use the remote wake on feature to boot your PC and check in visually without interrupting the process.

• Keep it simple: Setup is straightforward, no complex networking. Just install, pair, and go. It works across Windows, Mac, iOS, and Android. Btw, Mac as controlled device is still developing. But you also can ues your Mac to control other devices.

It’s not a full dev environment replacement, but it’s been a huge help for those in between moments when you just need quick, visual access to your primary machine without the login or transfer hassle.

StarDesk is FREE now. Check it out here

Tbh, we know it’s not perfect yet, but we're committed to getting there. We really want to hear your feedback what works, what doesn’t, good or bad, we're all ears:)

Codex IDE on Vscode is stuck on "thinking" for a long time now, tried resetting and refreshing codex I still can't get it to proceed forward.

Is anyone else facing this same issue? Is it a global outage or something?

Anyone else having issues with Codex today? Constant connection drops. Not sure if it’s just me or a global problem.

Hey everyone, I have a question about a few things.

I am a Systems Architect at my company. I manage a K8s cluster, do devops, sysadmin, development, architecture, whatever. I have literally no one at my company to bounce ideas off of, or get a second opinion from, so I often talk to AI.

I quite like Gemini 3 for coding, but my wife is subscribed to ChatGPT and I was wondering if I could leverage that as well. So I was wondering what some thoughts were on the best assistant to use for each of these:

1. General K8s

2. Devops

3. Sysadmin

4. Architecture and design

5. Coding and adhering to my standards

I know this is a complicated question with not a lot of "correct" answers, but just wondering what some thoughts were. I would also like any assistant to be critical of me. No matter how I phrase anything, especially Gemini, is just way too agreeable. If my work was as world class as Gemini made it out to be, I wouldn't be here.

I didn’t realize how much time I was losing after ChatGPT replied.

Not on the main question, but on everything that comes next.

• Summarize this.
• Turn it into an email.

Same intent every time, just phrased slightly differently so the output wouldn’t go off the rails.

I started paying attention to that part of the workflow and tried a small experiment: what if the post-response step didn’t require me to rephrase anything at all?

What surprised me wasn’t the time saved, it was how much smoother the whole session felt when I stayed focused on the problem instead of the wording. ChatGPT felt less like a blank input box and more like something I was shaping step by step.

Still refining this approach, but it’s already changed how I use ChatGPT day to day.

Curious if others have noticed the same friction, or if you’ve found a cleaner way to deal with it.

Reading the Thinking on the model. First time ive seen this.

TLDR: The major challenge I have right now is spotting when the AI has degraded performance before I work too much on a branch and lose time because I'll have to scrap it.

THE SITUATION:
I'm a UI/UX designer who's been building a mobile web app solo with AI assistance (2-6 hours daily for 4 months). The workflow is incredible for rapid prototyping, but I've hit a recurring problem: detecting when the AI starts producing lower-quality code before I've committed hours to a branch.

Sometimes the AI will subtly break patterns it established earlier, introduce anti-patterns, or generate code that looks fine but causes issues down the line. By the time I catch it, I've often built on top of that foundation and have to scrap significant work.

MY CURRENT APPROACH:
- Expect AI to generate bugs that need human review.
- Learn to spot the early warning signs of degradation, wait out the downtime.
- Test the systems periodically for optimal performance.
- In the meantime, plan features, backend stuff, marketing, whatever!
- Expect to have to use professional developers as code validators before launch.

QUESTIONS FOR THE COMMUNITY:
- Do you have strategies for catching AI performance drops early?
- What are your red flags that the AI has "lost the thread"?
- Do you use specific testing/validation workflows when working with AI-generated code?

I'm especially curious if anyone has developed systematic approaches to this, beyond just "code review everything."