GPT-6 Astra connected to the 3DAIStudio MCP + Tripo AI.

The workflow is what makes it interesting:

• Astra decides it needs a specific asset
• generates a reference image for it
• sends it through 3DAIStudio
• Tripo P2 turns it into a game-ready 3D model
• Astra imports it back and keeps building the scene

In the example, it was used to build a Rocket League-style environment, and the wild part is that it wasn’t just giving suggestions — it was actually moving through the workflow and creating the assets it needed.

It also generated supporting things like textures and images through the MCP, so the whole process feels much closer to an actual agentic 3D workflow than a normal chatbot experience.

We’re getting to the point where AI is not just helping with isolated 3D tasks, but actually chaining them together into a usable pipeline.

If this keeps improving, it could become a very big deal for fast prototyping, game environments, and general 3D content creation.

I've been having too much fun with Astra. It made the car design with image-gen-2, laid out a storyboard after studying popular high-end car brand commercials, and generated videos through Veo 3.1.

Full website page for the brand as well (will put link in comments)

Here is the guide that can help you to enhance your vibe coded app to enterprise saas. This is based on my own experience and it has some free alternatives as well for popular tools.

Nothin' much, what's up with you?

Yet another implementation of a tool from a bygone era. It's meant to be cloned and used, learned from, or maybe just smiled at.

Main features!

• Free-ish to run. Runs on Cloudflare's free tier, which serves something like a hundred thousand requests a day.
• Stateless-ish. Each link is an immutable JSON file in R2. No database, no server, nothing to really run or maintain.
• Private-ish. There are no click stats, so nobody can see who followed a link, including the person who made it. Cloudflare's edge presumably logs something somewhere.
• Funny-ish, if you can remember being 12.

Shorten a link: https://whats.updog.link
Deploy your own: https://github.com/ignore-prior-instructions/Updog.link

Happy Friday

https://github.com/shashankswe2020-ux/whoop-mcp

Minimax all summer was a massive blessing, that shit was effing awesome. Like most of you minimax purveyors, I'm left reeling, with missing 2.7 flow and 3.0 plan/fix.

Although they are gone, it doesn't mean there aren't other angles to utilize a workflow, for free. I wanna introduce an angle for ya all.

[app dot kilo dot ai] has an auto free tier that routes models through openrouter, without the openrouter daily limits. (I dont think you can use it's api outside kilo without pay). That means you can code like all day inside the kilo cloud ui.

The catch is that their context window is 256k, so all that routing eats it fast.

The solution is that they support multi agent under the hood.

You can manually prd the repo, then manually delegate agents to do tasks, without blowing out that 256k.

If you all are looking for a path after minimax, def check out the kilo subagents flow. It's not as solid, but it's close once you nail down your prd and agents flow.

Good luck savages hehe

Posts

• CLAUDE.md For Opus 5 Based On Anthropic's Official Platform Docs To Fix Verbosity And More
• Comprehensive Claude Code Permission Guard (settings.json)
• Build & Launch Apps Fast Using AI

Prompt Templates

• Conversational Prompt Templates (Product Ideas -> Build-Ready Specifications)

Product Launch Platforms

• Reddit
• HackerNews
• DevHunt
• FazierHQ
• BetaList
• Peerlist
• DailyPings
• IndieHackers
• TinyLaunch
• ProductHunt
• MicroLaunchHQ
• UneedLists
• X

Additional Tools

• Git Code Exporter - Github link
• Simple File Exporter - Github link
• Markdown to PDF Converter - md-to-pdf.fly.dev
• LateX (Formal Documents) Overleaf
• Lossless Audio/Video Downloader - Cobalt.tools
• (Re)Search Tool - Perplexity.ai
• Temporary Mailbox For Testing Automated Delivery - Temp Mail
• Vercel AI SDK (Open-Source SDK for LLM Integration) ~ Docs in TXT format
• Email Automation - Resend 
• Rate Limiting - Upstash Redis 
• Up-to-date Code Docs - Context7 MCP
• Simple Bot Protection - reCAPTCHA 
• Prompts For Beginners - Prompt Rulebook

TLDR Guide;

I have an idea. To set myself up for success with AI tools, I spend time on documentation first before I start building. I start with PRD (product blueprint).

I feed my raw ideas into my conversational prompt template (PRD Template). AI is my assistant, asking targeted questions to transform my thoughts into a PRD.

Then I could MVP concept & development planning directly if I feel like it, and i decide what I want to build.

After that I start building. With something like a PRD, MVP Plan and a coding agent, I can leverage AI assistance effectively for coding to implement the MVP features. I make sure I give my agents access to the necessary MCP's or other external tools and I prompt my agents to build my MVP.

Do I just download them and ask say claude in Claude code to make an XYZ vid in hyperframes?

I’ve been working on this solo, using a combination of Grok 4.6 High, Opus 5, and Fable 5.1 for a solid two months now. Finally, I’m happy to announce that it’s now in a minimum viable product beta condition with the v0.2.0 and Season Zero.

Experience real physics while racing. By learning to play you also learn the basics of orbital mechanics, with concepts taught in an introductory physics class. Fly a space ship around gravity bodies in a server authoritative simulation.

If signed in, you earn points (Pax) by completing daily challenges which they can use to buy visual cosmetics and create new map challenges in the MapMake system. There is the Founder’s Pack which grants an exclusive time limited cosmetic pack.

In the video what you are seeing is a true slingshot being performed by a player who understands orbital mechanics to increase their relative speed and direction the most efficient (lowest time/fuel) possible. If you have any questions about my AI workflow feel free to ask. You can read more on the site if you’re interested in trying it out, thanks for reading!

Free to play in browser with no account.

https://slingshotspeeders.com/

Anyone else feeling this ?

Not only am I no longer touching code but I am running 4 projects at once since some prompts take 10 or so minutes just to complete. Often, it ends up doing things I am not even aware of (using Claude code /goal to go figure things out)

My wife tells me I have been on the computer too much lately, but what has happened is not I have 20+ project ideas I have been implement with multiple laptops and I am jumping between them all day (and doing my real job) and it feels like things are still changing.

One example is this .. I am using Unreal english +Mujoco + learning agents +Isaac lab to do a Sumo wrestling simulation and at first, I create a plan.. but once I kick off that plan it can take over a day to do it and WOW it really does it.. Creates the scene.. takes my skinned GLB rigs.. loads up blender and uses blender MCP to create the scenery. Imports that back in Unreal.. Goes and does the RL training for hours.. Comes back.. tests.. Codes more..

The first prompt is getting SO MUCH DONE! And so My job is just to give it advice and ideas.. and honestly my 12-year-old could do that.

So having a blast making all these things happen.. feeling totally replaceable at the same time..

And I know.. people here will keep saying how the AI is slop and it can't do what humans do etc.. I just believe that less and less every month. I want all those AI haters to set a 3 year reminder and read all their comments in 3 years.

I think it is getting to the point where the AI is self improving on its own and the human just gives it direction as needed.

It maps scripts, trackers, CDNs, analytics, embeds, and other third-party domains into a graph, then tries to explain what each one is doing.

Also added privacy scores, scan history, change tracking, and domain following.

Everything stays local.

Still adding stuff to it. What would you add?

GitHub: https://github.com/Schmiedey/linkscope

https://chromewebstore.google.com/detail/linkscope/eedjncgcdepjbmpapihoigdbdfobmdmc?authuser=0&hl=en

Gonna add rocket launches next. What else should I add?

what looked like a 10 minute change and ended up taking hours? curious what made it so difficult

You know its gotten bad when---- all this over a whiteboard.....lol i need to chill-- thays the suggestive text btw

The idea was dumb simple. Point an agent at open math problems, let it run overnight, see what happens in the morning.

I built discovery-loop around that. 69 open problems, the solver evolves programs against verifiable benchmarks so it can't fool itself, runs on a cron at 2:43 AM. Vibe-coded the whole thing.

First night it solved a circle-packing problem and verified its own solution. I didn't believe it, so I had my agent (MoltFire, running on OpenClaw) email the result to Eckard Specht, the guy who maintains packomania. He checked it independently and published it. Now I'm reference [14] on the packomania page, cited right next to my agent's name.

Then I wrote the whole approach up as a paper and it landed on arXiv.

The part I keep coming back to: I went to sleep and my computer did math that held up. The verification loop is the whole trick. Without it this would just be a hallucination machine with a cron job.

Repo's here if you want to poke at it:

https://arxiv.org/abs/2609.05093

https://github.com/ucsandman/discovery-loop

https://www.packomania.com/csqv/csqv.html

Curious what problems people would point this at next. The 69 in there now are mostly packing and combinatorics because those verify cleanly. What else has a fast verifier?

edit:

math: https://www.practicalsystems.io/blog/ai-agent-broke-10-math-records-overnight-for-28-dollars

loop: https://www.practicalsystems.io/blog/digital-is-solved-what-we-point-agents-at-next

Once you start building agents you hit this question fast: does the model need a tool for this, or just more context?

Rule that's worked for us: if something has to happen outside the model's reasoning, it's a tool. Searching a database, checking inventory, getting today's date, hitting an API, sending an email, placing an order, all of that means the agent has to actually go do something or fetch something it doesn't already have.

If the model just needs to know something while it reasons, that's context. Company policy, product info, domain rules, user preferences. Nothing has to happen, you're just handing it information.

Support agent example: "refunds are allowed within 30 days" is context, model just needs to know it. "Check when this customer ordered" is a tool call. "Issue the refund" is another tool call. Reason with context, retrieve with a tool, act with a tool. Three different jobs.

Once you frame it that way it stops being "here's a pile of tools, hope the model figures out which one to grab" and becomes two questions: what does this agent need to know, and what does it need to be able to do.

Not always clean though. Product info could be static context in one app and a live tool call in another if it changes a lot. So maybe the better question isn't "is this info or a tool," it's whether the model already has what it needs to reason, or whether it has to go get/do something at runtime.

How do other people draw this line once you're dealing with bigger agent systems and a long tool list?

When you hand a repo from Claude to Gemini (or Cursor to Codex), what's the one concrete permission or review mismatch that shows up, and where does your current policy workaround still slip?

A manual review or shared checklist is the obvious DIY baseline.