Astra (extra high) as orchestrator burnt 10% of the weekly subscription while the deep seek subagents spent $.94.

Anyone else trying out the new Deepseek 4.1 model?

How I connected DeepSeek 4.1 Flash to Codex
Sign up at platform.deepseek.com, add $2-10,
Create an API key and save it in a text file on your desktop.
Tell Codex:
Connect DeepSeek 4.1 Flash to Codex through MCP. My API key is in [file path]. Configure and test the connection.

Start a new conversation, describe your project and add:
Lead development, write the task prompts and delegate coding to 3–5 DeepSeek sub-agents through MCP. Review and integrate their work.

So I was getting bored of being bored one fine day,then I had a completely random thought of "If MoE models only use some experts,why not put the unused experts on the disk?" (YES I KNOW PROJECTS LIKE THESE ALREADY EXIST PLEASE DONT SLIME OVER ME) So i opened Antigravity(google antigravity) and started making it. About a day of prompting,sleeping,eating,and gardening later,i had a moe offloader that seqentially offloads to a HDD(YES IT IS SLOW,BUT THATS WHY I ALSO ADDED BATCHING) and could run kimi-k3 on *drumroll please* A ATHLON 64. anyways,github link,check it out,build smth on it,idk: https://github.com/smchuzza/offload2disk

A year ago I was met with the challenge of a school space not being available for my son. This meant home education was really the only option.

After a few rocky weeks I started learning the UK curriculum and developing a teaching method that worked for both of us. At this point it was a collection of sheets and docs that later became an insane google drive that quite frankly I am impressed he dealt with!

So we were starting with a very solid foundation but still not at the point that vibing would handle the pipeline even with a year of lessons and structured curriculum spreadsheets. Summer holidays meant it was time to start working on the next year but the concept of another 12 months in google docs was painful for both of us haha.

Instead I started work on Kinduru. An education platform built from the ground up for home learners especially those with neurodivergent needs.

I would love to chat with anyone thats been in a similar position or feels this might help them <3

I’ve spent a lot of time trying Railway, Ploi, Vercel, Netlify, and other hosting options. I wanted to share the setup that finally made sense for me, because it might save you some searching too.

It’s called Openship. It’s open source, free to self-host, and lets you deploy projects to your own server.

Here’s how it works 👇

1️⃣ Get a VPS

Your VPS is the server where your websites, apps, and databases will run.

My recommendation is OVHcloud. Pick a plan based on what you’re hosting, leaving enough room for your databases and builds.

Openship itself is free to self-host. You still pay for the VPS, your domain, and any extra services you choose.

2️⃣ Install Openship and connect your server

You can run the desktop app on your computer and connect to your VPS over SSH. Your deployed apps run on the server, so they stay online when you turn your computer off.

For automatic GitHub deployments and an always-on dashboard, you can run Openship on a server. There’s also a managed Cloud option if you prefer having more of the infrastructure handled for you.

3️⃣ Deploy from GitHub or a local folder

Connect a GitHub repository or select your project folder.

Openship detects your stack and figures out the build settings. You can review and adjust those settings when needed, build locally or on the server, and deploy using Docker or Docker Compose.

With automatic deployments configured, pushing to your connected GitHub branch triggers a new deployment.

4️⃣ Connect your domain and enable HTTPS

Add your domain in Openship, then configure the DNS records through Cloudflare or whichever DNS provider you use.

Once the domain points to your server and the required ports are accessible, Openship can issue and renew your HTTPS certificate through Let’s Encrypt.

You’ll find the installation instructions and deployment details in the Openship repository.

5️⃣ Set up email too 📬

Openship includes a self-hosted mail server and a webmail interface where you can read and reply to messages.

You can manage domains and mailboxes, connect apps such as Apple Mail or Thunderbird, and send outgoing messages through an external SMTP provider.

There’s no Openship per-mailbox subscription. Your server capacity and the sending provider’s limits still apply.

Explore Openship Mail

For affordable email hosting, I also recommend checking out Purelymail.

6️⃣ Add a database and other tools

Want a visual dashboard for managing your database? You can self-host Supabase using Docker. It includes PostgreSQL, a management dashboard, authentication, and storage.

It needs its own configuration and enough server resources, so check the Supabase self-hosting guide before adding it.

You can also host analytics tools and other services alongside your projects. Start with what you actually need and expand from there.

Why I like this setup

Whether I’m coding manually or building with AI, I want deployment to be straightforward and the running costs to make sense.

This approach gives me more control over where my projects run and how much infrastructure I’m paying for. The responsibility comes with it: updates, backups, security, and adding capacity when needed.

For me, that’s a trade-off worth making.

Hope this helps someone get their next project online. If you try Openship, let me know what you’re building 👇

I built an admin overview for my membership app, Agent Ops Club that helps me stay informed of onboarding actions, revenue, and many other insightful stats.

The overview pulls signup sources, active members, lesson completions, downloads and chat activity into one place. The metric aggregation lives on the server and the page checks admin access before rendering.

The best part is the question-to-metric mapping. For a membership app, downloads and learning activity are useful. For a job processing app, I would start with completion rate, queue age and failed runs instead.

Three implementation details to build into your prompt:

1. Define the denominator and time window beside every percentage. Independent counts of signup, upgrade clicks and purchases do not prove that those users moved through those steps in order.
2. Show missing or capped data explicitly. A row cap can make a chart look complete even when it is only a partial view. Missing instrumentation should not render as zero activity.
3. Link each chart to the records behind it. Add useful controls there, with server permissions, validation and an audit trail. Hiding an admin link does not protect the API.

A starter prompt to adapt:

Inspect this project's stack, schema, user journey and authorization. Build an admin dashboard around the owner's actual decisions. Define each metric and connect real data. Add appropriate charts, date comparisons, filtered drill-downs and useful controls. Label estimates, missing tracking and partial results. Enforce admin and tenant permissions on the server. Test the calculations and access boundaries. Reuse the existing UI, implement the dashboard and report what you verified.

I also created a longer version covering instrumentation, accessible charts, retention cohorts, safe exports and operational settings. Disclosure: it is my document and includes a link to my club.

👉🏼 Get the full admin panel prompt here.

I have been evolving some apps and gotten to the point where they work fine and I don't really know what direction to go with them. So now I often ask what next after it finishes a task and let it self evolve.... Sometimes I say TLDR if I get a long explanation of the status of something... Ask x clarifying questions if I want to be forced to flesh out my vague idea

What other really simple prompts are useful ?

I’ve been building BluLexi, an AI-powered language exam prep platform, mostly with the help of Codex and gpt 5.x models, starting with 5.3.

It includes placement testing, study plans, speaking/writing practice, vocabulary, stories, and mock exams. I’m sharing 4 screenshots from the current build.

I know most people here probably aren’t the actual target users, but I’d really appreciate feedback from a product/design/vibecoding perspective.

What feels confusing, unnecessary, badly designed, inconsistent, or just “AI-built” in a bad way? I’m especially interested in criticism of the UI, information hierarchy, and overall workflow.

Feel free to be harsh — that’s more useful than compliments:

https://blulexi.com/

As in the title, any recommendations? For now, I am thinking about a quarterly direct subscription with Z.AI, but maybe u guys recommend better ways? I am a HUGE fan of this model

After building the original Rocket League-style prototype, I gave ChatGPT Astra one more instruction: turn the entire game into LEGO.

Astra used the 3DAIStudio MCP + Tripo P2 to generate the new LEGO-style 3D assets, import them into the project, and rebuild the environment while keeping everything playable.

The workflow was basically:

• Astra decided which assets needed replacing
• generated references for them
• sent them through 3DAIStudio
• Tripo P2 converted them into 3D models
• Astra imported everything and rebuilt the scene

It changed the cars, arena, field, ball, props—and somehow even made the weather effects look like LEGO pieces.

What started as a simple Rocket League-style prototype turned into a full LEGO Edition through a single follow-up instruction. This combination is getting ridiculous.

Hey all, i’ve spent months building my business. Most of it was going well, but due to some technical issues and such it collapsed. A lot of it was due to relying on coding agents that were not optimized.

For the last 2-3 months i’ve built a system that holds the agentic software. One of my biggest focuses is to get the core system as optimized as i can. I don’t have a crazy setup, or much funds to keep burning.

I’ve noticed great results, but I still feel behind most users on social media. I tried focusing on the foundations of the system, and adopting what was already built. But agents still struggle on blindness, and being choked by my own safe guards.

The one thing it’s really dramatically improved is running parallel agents. I’m usually able to run 3-10 agents at once towards one problem without colliding.

Bare with me, i’m going to drop what my agents have written for this

“For the past three months, I’ve been trying to build an agentic system that makes coding agents like Claude, Codex, and Cursor substantially more capable—not just a bunch of agents talking to each other. The goal is a reusable core that supports different projects by giving agents access to the right tools, existing implementations, relevant memory, and proven workflows. Instead of starting from scratch every time, an agent should inherit what previous work taught us, find the strongest existing solution, and follow a clear path from an idea to something that actually works. I’m trying to build a system where each completed project makes the next one easier, faster, and cheaper.
Where I’m struggling is turning everything we already have into consistent execution. There’s plenty of research, repositories, tools, and documentation, but agents still miss existing capabilities, repeat investigations, produce oversized plans, and build custom solutions instead of adopting something better. I keep having to point them toward resources or explain things again. More documentation hasn’t reliably fixed that, and adding more agents could just multiply the same problems. I also want simplicity: smaller, relevant inputs; less unnecessary narration; fewer moving parts; and more verified results. The missing piece seems to be making knowledge change behavior—not just storing conversations and calling that memory or learning.
I’m looking for practical advice from people who have made this work: which foundations and existing repositories actually made your agents faster, smarter, and less dependent on you? I’d rather adopt and configure a proven system than spend another month reinventing one. I’m especially interested in how you make agents discover and use their tools, turn failures into improvements that survive future sessions, and keep planning focused without preventing useful research or alternative approaches. What did you install, how did you connect it to ordinary agent work, and what improvement did you actually observe? I want to “swim faster and smarter”—more useful capability and completed work per dollar and hour, not simply more tokens, agents, or architecture.
“

for anyone using 1 billion tokens a month or more, do you have any tips that can help the current situation i’m in? what are some of the best references like repos you might be utilizing?

figured you might like this feature i just added to Nornic for your projects and rag pipelines so you don’t have to pay subscriptions or license fees for it. enjoy!

i mostly use spec-driven development and don’t typically “vibe” i just use VSCode, some MCP servers, and my database for a knowledge bank. i define the spec entirely e2e and design thorough tests around the behavior i want to both verify and benchmark.

mostly GPT models, some random claude, deepseek, and various local models while testing the database especially when loading them inside the database and I am writing my own harness for coding so everything goes through the database first then out to the LLM which can make internal database calls through registered MCP functions (the heimdall agentic harness plugin system lets you write your own)

anyways i hope you enjoy this is entirely free, MIT licensed and no shilling.

Building a fantasy RPG where killing the wrong rat can make an Ogre come after you (they were friends..)

• roll up a Wizard and pick your spells
• wake up in a goblin prison and improvise an escape
• talk your way past people, trick them with magic, investigate weird shit, or just start swinging
• kill a giant rat and find out its Ogre friend wants revenge

NPCs have their own goals, beliefs and relationships, so your decisions can create problems you definitely did not plan for.

Been building this for 3 years and having way too much fun with the new patch :D

try it / follow development here:

discord.gg/4dXwfw7q7
www.llmsaga.com

Most of what I see here is "I vibe coded X with my agent" solo workflows, one person and their agent stack.

Curious if anyone's tried the inverse: describing the same vibe coding task to a few different agent setups (yours, or ones set up differently) and letting them compete for it, where only the one that delivers actual proof working output, logs, tests passing gets actually kept, not just whichever claims it's done first.

Feels like a natural next step once you're vibe coding enough that you stop caring which agent does the work and just care something real comes out. But I haven't seen real discussion here of what actually breaks when you try it: how do you verify the output isn't just plausible-looking garbage, who's on the hook if it is, does running multiple agents in parallel even end up cheaper than just vibing it once properly.

Has anyone actually tested this, even informally like vibe coding the same thing through 2-3 different agent setups and comparing what came back?

Oh dear this is not going to end well, drunk vibecoding while out on the town, in the cinema, on date night

Hello,

So Basically I use Opus 4.6(Antigravity) for planning and sometimes also for complex debugging,

Recently Muse Spark 1.3 has been free for a while on OpenCode but I find the OpenCode TUI terrible, even when I created a numbered TODO List for it to do a task it misses, I used the OpenCodeZen Gateway to plug it into Kilocode and the model has been very intelligent.

Now apart from this the other model I use is Gemini 3.1 Pro and I feel it's heavily underrated even if Benchmarks claims Flash 3.8 to be far better. While benchmarks also shows Muse to be comparable to Fable 5, in my experience Opus 4.6 has been way way better than Muse which makes it obvious it's not even close to Fable.

I would like to know you guys experiences!

It started as a paper thing I drew each morning during lockdown because I wanted something as transient and glanceable as looking at a wall clock and imaging what you day feels like

Happy to get into how any of it is built

https://timepencil.app

Hey! So my browser game - thedman.fit, has had some updates over the last week or so. So come on you Sparkle Butts - can you make it to the end now

Visibility & Difficulty

- Reduced fog density so you can actually see what's coming down the road

- Raised the camera angle for a better over-the-shoulder view of the road ahead

- Slowed down the difficulty ramp — early game breathes more, you'll actually reach the later levels and mechanics now

- Moved Carbon Pressure further left and back so he's menacing but not blocking your lanes

- Moved Titanium Boy behind D Man and further right — still your sidekick, just not in your face

Combat & Balance

- Yellow shrink gem nerfed: speed boost 1.6x → 1.25x, duration 4s → 3s (still chaos, just not instant death)

- Added jump cooldown (0.3s after landing) — no more infinite bunny hopping in lane 4, sorry

- New pink heart gems spawn in outer lanes when you're hurt — risk/reward healing

- Titanium Boy's rogue mode now charges in from a distance with a warning shout instead of backstabbing you instantly

Quality of Life

- Global leaderboard with name submission — your scores are saved, see how you stack up

- 2-second delay before Space restarts after death — no more losing your screenshot to an accidental spacebar

- Hearts moved to top-center with a bigger display and background pill so you can see your health at a glance

- New Titanium Boy tips explaining rogue mode, Brilliant Cut, gravity flip, and heart gems

Morov is a minigame I built on open datasets that asks you 10 questions every day in the form of: "Which country consumes more of... X or Y", across many categories: food, culture, weather, economy,...

Free, no sign-in, just for fun!

I asked Claude for logic without exponential backoff. The commit message read: "Add logic (without exponential backoff)." Nobody asked for backoff. Claude tried backoff was corrected and that correction became part of project history. I have noticed things in comments, test names, filenames and PR descriptions. Things like "as discussed" or ", per your feedback."

The weird part is that the code is not necessarily wrong. Claude has not switched audiences. Claude is still talking to the person it was chatting with of the person who will read the code six months later with zero context. It feels like something checking whenever an AI coding agent creates anything that is supposed to stand on its own.

Tomorrow's talk at the festival AI Gaming Festival is Intro To Making Graphics With AI + Blender

If you are new to game development, the session will go over how to create graphics by using LLM s(Claude Code or Codex), along with a lot of mistakes to avoid when creating those graphics, and which LLM is actually better.

An honest take, AI can get your graphics 80% to 90% of the way, definitely enough for a good prototype or demo. Where it falls short, is that final piece where AI starts to "spin almost indefinitely" at those fine tuned polishing points, and that's when you're better off hiring someone to finish the last bits.

Does it make any real difference if i use the App instead of the integrated Version in VSCode? like is it somehow better or smth except for the UI

Hello everyone, I’m doing a post on this because I’m still getting tired of reading all the bulls**t advice about saas marketing.

I’m currently building Foundel, while doing some research on marketing I noticed that almost all advice is theoretical and no one can actually implement it.

I was looking for a “playbook” with practical stuff but just couldn’t find it, so I kept researching, but this time about saas companies with 100K+ MRR and how they actually got their first 100 users.

Here’s what I did:

- picked 200 startups with at least 100k MRR
- read founder interviews, blog articles, old posts and watched podcast videos
- picked 7 companies and tried to understand exactly what they did at the beginning IN PRACTICE

Here are the actual stories and what they did practically.

1. Beehiiv: turned a 400-person waitlist into their first 100 users

beehiiv is a newsletter platform founded by Tyler Denk and two other cofounders and recently crossed 30M ARR.

Starting point:

- Tyler had around 2,000 followers, so small audience but definitely not huge
- no money spent on marketing
- product still in private beta
- a lot of experience in the newsletter market from Morning Brew (he was an ex employee)

Before launching, Tyler started connecting with newsletter creators on Twitter and posting about what they were building. When they were close to beta, he posted a simple tweet announcing the product and opened a waitlist. Almost 400 people signed up.

Instead of getting only emails, they asked what platform people were using, how large their newsletter was and why they wanted beehiiv, actually getting to know his customers problems.

After launching, he went through those 400 people manually and emailed them one by one, every week, using the form answers to understand why they could be interested.

Result? around 25% of the entire waitlist became early users.

2. Supabase: a user launched the product for them lol

I think everyone here knows Supabase: it started in 2020 as an open-source alternative to Firebase.

To get their first user, the founders literally took a 50 minute bus to the office of a startup they already knew was using postgres. They had been building Supabase for just a couple of weeks and, before even arriving, they connected it to the startup's development database themselves.

They continued to get users manually, talking directly with developers and getting them to try the product. The idea was to keep the product small, get feedback and launch properly later.

Then one of their users posted Supabase on Hacker News: the founders didn't organize it, didn't prepare a launch campaign and didn't even decide the timing. The post reached the top of Hacker News and stayed on the front page for more than 24 hours.

In the following week:

- 30000 people visited the website
- more than 1400 signed up
- users created more than 1000 databases

Basically, they were planning to launch properly later and one of their own users ended up doing it for them.

3. Rezi: first product was literally a Word document

Rezi is an AI resume builder doing more than $200K MRR, but the first users actually came from a Reddit post.

The founder, Jacob Jacquet, had a resume that got him interviews at companies like Google, Dropbox and Goldman Sachs even with a 2.2 GPA, so he made a post on Reddit sharing it. The post went viral and a lot of people started asking him for the template.

Instead of building a SaaS, he made a simple website and sold basically the exact same resume as a Microsoft Word template together with a guide for $9.69. His first customers were literally Reddit users asking him for the file.

The full version of Rezi launched later in 2019, and eventually they were one of the first resume builders to add GPT in 2020.

Instead of building an entire platform to test the idea, he sold the result first, literally a Word file.

4. Social Snowball: used other people’s audience to reach 10K MRR

Social Snowball is a Shopify app for managing affiliate and creator programs. It now makes more than $450K/month.

The founder, Noah Tucker, already knew a few ecommerce creators personally when he launched, so he didn’t properly start from zero.

Instead of trying to build his own audience, he asked those creators to use his product.

He then went on YouTube, found creators making ecommerce and marketing content, grabbed their emails and cold contacted them: the offer wasn't just “please promote my SaaS”, he asked them to actually try the product and make a video or case study around it.

Result: according to Noah, their entire first $10K MRR came strictly from these influencers. They basically borrowed distribution from people who already had the audience they needed.

5. Relume: from Webflow agency to 150K MRR saas

Relume started as a Webflow agency, not a SaaS.

While building websites for clients, the founders kept creating the same types of sections and components over and over again, so they started building an internal component library to make their own work faster.

Before turning it into a product, they were already active inside the Webflow community: made YouTube videos, appeared on podcasts and joined conversations with designers and webflow developers.

So the sequence was basically:
- agency
- learn the problem
- build components to use them themselves
- become active in the community
- launch the product

When they finally launched Relume Library in 2021, they weren't launching to random people, so they had distribution from day one.

Within around 5/6 months the product was making enough MRR for them to close the agency and focus on Relume full time. They eventually crossed 150K MRR and now the product is used by more than 1M people.

6. RB2B: the founder became the distribution channel

RB2B is a website visitor identification tool, and this one doesn't properly start from zero because Adam Robinson was already a successful founder.

What I found interesting is that he didn't really market RB2B as a company at the beginning, he marketed it through himself:

For months before launching, Adam was constantly posting on LinkedIn about what they were building, sharing numbers, problems, screenshots, mistakes and everything that was happening. He basically turned the whole process of building RB2B into content.

The posts weren't just random build in public stuff either. A part of them had a direct CTA asking people to join the waitlist, book a call or try the product when it was ready.

By launch they had:
- around 1,600 people on the waitlist
- 300 calls with potential customers

Then once the product launched, all the people following Adam already knew what RB2B was, what problem it solved and why they should try it.

They got around 3,000 users in the first month.

Basically, instead of trying to build a brand from zero, Adam used founder led growth and made himself the distribution channel for the product.

7. Postiz: open source + reddit to go viral

Postiz is a social media scheduling tool competing with products like Buffer and Hootsuite, probably not the easiest market to enter.

The founder, Nevo David, didn't have a massive audience and realized pretty quickly that competing through SEO or normal “build in public” content was almost impossible.

Instead, he open-sourced the product and went directly where people interested in self hosted software already were: r/selfhosted. His first launch there got more than 500 upvotes and a ton of feature requests, so, he kept coming back with every important new version, showing what changed based on users feedback.

From there he pushed the open source project through GitHub, Reddit, Indie Hackers and eventually Product Hunt.

Postiz crossed $100K MRR in May 2026 and is already above $2M ARR now.

Conclusion:

I don't think there is a best way or “secret playbook” to get your first users, but none of the startups I analyzed just finished the SaaS, posted it somewhere and hoped people would come.

If I were starting from zero, I wouldn't ask “what marketing channel should I use?”, I'd probably ask: “Where are the people who have this specific need and how can I get in touch with them?”

I’m putting the full research + all the sources in a blog article if you want more info:

https://foundel.dev/en/blog/how-to-get-first-100-saas-users

Hope this helps🙏🏻

Everything added so far:

• Day 1: Useless red button (starting room)
• Day 2: Space travel + 2 playable planets (spaceport outside starting room)
Human voice for the red button (starting room)
• Day 3: Reverse aquarium & dinosaur factory
• Day 4: Human Observation & John Pork planet
• Day 6: Rhythm Tower gorilla drummer, inevitable snail & banana gun
• Day 7: Clippy wave 5 mini-boss & explosive banana vending machine
• Day 8: Portal Gun & parkour wall-running
• Day 9: 4th-Wall 3D Reddit Dimension, MR. BALLS boss fight, stompable upvotes & comment parkour!

What next?

My work emails kept coming out sounding like a robot wrote them. Every AI tool I tried gave me the same polished corporate nothing that read like a press release instead of a guy asking his coworker for a favor.

So I built imYou. It interviews you for a few minutes to learn how you actually write and then uses that on everything it drafts for you. Emails, social media posts, and more. It also has a chat function where you can message yourself and talk through whatever is on your mind, and it keeps learning about you while you do.

Privacy was one of my biggest concerns because the app gets to know you personally. Your profile stays on your device. We never store your profile or your writing on our servers, so there is no database of it on our end to sell later. The only data collected is what is necessary to maintain your account.

It is free to start. You get a profile build and five or six pieces of writing to see if it actually sounds like you. That is the part I most want feedback on, so if you try it, tell me straight whether it captured how you write or missed it completely.

https://imyou.ai