u/LearnHiveLabsUSA

Started back in June — attackers were adopting orphaned/abandoned AUR packages (normal community feature, lets people pick up maintenance when someone disappears) and then quietly slipping malicious code into the PKGBUILD via follow-up commits. Over 1,900 compromised packages got purged. Arch devs called it clean by mid-June.

Then July 29 happens. New wave, kicked off through a package called openconnect-sso. Security researchers at IFIN dug into it and it's actually a pretty slick two-stage infection:

Stage 1: loader that checks for debuggers/sandboxes/VMs first (so it doesn't get caught by researchers), then sets up persistence via systemd services + cron jobs

Stage 2: pulls a Rust-based stealer+RAT from a Tor .onion server. Targets browser creds, crypto wallets, cloud secrets, AI API keys, and can worm laterally over stolen SSH keys

By July 30, over 200 packages were hit, including some fairly popular ones (boringssl-git, icloudpd).

That was enough for Arch to just disable AUR package adoption entirely — not the whole AUR, just the "take over an orphaned package" mechanism, since that's the attack vector.

The quote from the mailing list (Robin "Antiz" Candau, on behalf of Arch DevOps) is very much "we're tired":

"Due to the current influx of malicious package adoptions and follow-up commits made via the AUR, package adoption is currently disabled while we are handling the situation."

If you run Arch and use AUR helpers, now's a good time to actually read PKGBUILD diffs before you blindly update, especially for anything that recently changed maintainers.

A Free, Production RAG Pipeline in n8n (Gemini + Firestore + Vector DB + WordPress)

If you've tried running big research papers or books through the free Gemini API tier, you've probably hit the wall: 250,000 tokens per minute, then a wave of 429 errors.

The fix is RAG. Instead of dumping the whole document into the model every time, you build a small local index and only feed it the paragraphs that actually matter for the question being asked. Here's the setup I use, node by node, and it costs nothing.

The stack (all free tiers, no card required)

n8n — free if you self-host it via Docker or npm

Gemini API — free through Google AI Studio, up to 1,500 requests/day on Flash

Firestore — 50,000 reads and 20,000 writes/day on the free tier

A vector database — Qdrant Cloud (1GB free cluster) or Supabase (500MB with pgvector)

WordPress — the built-in REST API on any self-hosted site, no plugin needed

One thing to get right early: don't try to make Firestore double as your vector store. It's a document database, not a vector one — its free tier has nowhere to put embeddings. Use Firestore purely as a metadata log, and let Qdrant or Supabase handle the actual similarity search.

Workflow 1 — Ingesting and chunking a document

This fires when a new paper or PDF comes in. It splits the text and writes it into the vector store.

Nodes: Webhook/File Trigger → Read Binary File → Firestore (insert metadata) → Recursive Character Text Splitter → Vector Store node (Qdrant or Supabase) with a Gemini Embeddings sub-node attached

A few details that matter:

Log the paper's title, ID, and upload time to Firestore first — gives you a paper trail of what's been processed.

On the text splitter, set chunk size to 2,000 characters with 200 characters of overlap. The overlap keeps sentences that straddle a chunk boundary from getting cut in half.

On the vector store node, set the operation to "Insert Documents," then drag in a Gemini Embeddings sub-node using text-embedding-004 — it's free and handles the text-to-vector conversion.

Workflow 2 — Querying, rewriting, and publishing

This one runs on a schedule (or a manual trigger) to pull from the index, run it through the model twice, and push the result to WordPress.

Nodes: Cron/Manual Trigger → Question and Answer Chain → Basic LLM Chain (rewrite pass) → WordPress node

Step 1 — the RAG lookup. The native Question and Answer Chain node does the vector search for you. Attach a Gemini Model sub-node (gemini-2.5-flash works fine on the free tier) and a Vector Store Retriever pointed at the same database and embedding model you used for ingestion.

System prompt I use here:

"Analyze the retrieved chunks of the paper. Extract the core discovery, data breakthroughs, and structural methodologies. Write a comprehensive, deeply structured technical breakdown."

Step 2 — rewrite it so it doesn't read like a summary. Don't try to do this in the same step as the RAG call — splitting the two keeps you well under the token limit and the output is noticeably cleaner. Use a fresh Gemini Model node with something like:

"Take this technical breakdown and rewrite it as an engaging blog post. Cut anything that sounds AI-generated. Use short paragraphs and active voice. Output clean HTML ready for WordPress."

Step 3 — publish. Feed that HTML straight into the WordPress node, set the operation to "Create Post." I'd send it as a draft first and skim it before publishing — full autopilot is fine once you trust the output, but check a few rounds first.

Where this breaks down

Free-tier Gemini data may get used to improve Google's models, so keep anything confidential or proprietary off this pipeline.

RAG is strong for pulling out specific facts or localized themes, but it's reading a handful of chunks at a time — it's not going to give you a coherent start-to-finish summary of an entire book. That's a different problem.

Happy to share the raw JSON for the workflow if anyone wants to drop it straight onto their canvas, or help troubleshoot credentials.

If you've tried running big research papers or books through the free Gemini API tier, you've probably hit the wall: 250,000 tokens per minute, then a wave of 429 errors.

The fix is RAG. Instead of dumping the whole document into the model every time, you build a small local index and only feed it the paragraphs that actually matter for the question being asked. Here's the setup I use, node by node, and it costs nothing.

The stack (all free tiers, no card required)

n8n — free if you self-host it via Docker or npm

Gemini API — free through Google AI Studio, up to 1,500 requests/day on Flash

Firestore — 50,000 reads and 20,000 writes/day on the free tier

A vector database — Qdrant Cloud (1GB free cluster) or Supabase (500MB with pgvector)

WordPress — the built-in REST API on any self-hosted site, no plugin needed

One thing to get right early: don't try to make Firestore double as your vector store. It's a document database, not a vector one — its free tier has nowhere to put embeddings. Use Firestore purely as a metadata log, and let Qdrant or Supabase handle the actual similarity search.

Workflow 1 — Ingesting and chunking a document

This fires when a new paper or PDF comes in. It splits the text and writes it into the vector store.

Nodes: Webhook/File Trigger → Read Binary File → Firestore (insert metadata) → Recursive Character Text Splitter → Vector Store node (Qdrant or Supabase) with a Gemini Embeddings sub-node attached

A few details that matter:

Log the paper's title, ID, and upload time to Firestore first — gives you a paper trail of what's been processed.

On the text splitter, set chunk size to 2,000 characters with 200 characters of overlap. The overlap keeps sentences that straddle a chunk boundary from getting cut in half.

On the vector store node, set the operation to "Insert Documents," then drag in a Gemini Embeddings sub-node using text-embedding-004 — it's free and handles the text-to-vector conversion.

Workflow 2 — Querying, rewriting, and publishing

This one runs on a schedule (or a manual trigger) to pull from the index, run it through the model twice, and push the result to WordPress.

Nodes: Cron/Manual Trigger → Question and Answer Chain → Basic LLM Chain (rewrite pass) → WordPress node

Step 1 — the RAG lookup. The native Question and Answer Chain node does the vector search for you. Attach a Gemini Model sub-node (gemini-2.5-flash works fine on the free tier) and a Vector Store Retriever pointed at the same database and embedding model you used for ingestion.

System prompt I use here:

"Analyze the retrieved chunks of the paper. Extract the core discovery, data breakthroughs, and structural methodologies. Write a comprehensive, deeply structured technical breakdown."

Step 2 — rewrite it so it doesn't read like a summary. Don't try to do this in the same step as the RAG call — splitting the two keeps you well under the token limit and the output is noticeably cleaner. Use a fresh Gemini Model node with something like:

"Take this technical breakdown and rewrite it as an engaging blog post. Cut anything that sounds AI-generated. Use short paragraphs and active voice. Output clean HTML ready for WordPress."

Step 3 — publish. Feed that HTML straight into the WordPress node, set the operation to "Create Post." I'd send it as a draft first and skim it before publishing — full autopilot is fine once you trust the output, but check a few rounds first.

Where this breaks down

Free-tier Gemini data may get used to improve Google's models, so keep anything confidential or proprietary off this pipeline.

RAG is strong for pulling out specific facts or localized themes, but it's reading a handful of chunks at a time — it's not going to give you a coherent start-to-finish summary of an entire book. That's a different problem.

Happy to share the raw JSON for the workflow if anyone wants to drop it straight onto their canvas, or help troubleshoot credentials.

The use case: an agent that reads new emails and my notes folder, drafts replies or summaries, and remembers context across sessions — running on my own machine.

- Orchestration — LangGraph: this is the loop that decides what the agent does step by step (read email → check notes → draft reply → wait for my approval). I picked it because I wanted a human-approval step before anything gets sent, not a bot firing off replies on its own.

- Memory — Mem0: without this, the agent forgot everything between sessions. Now it remembers "she prefers short replies" or "this client always CCs their assistant" without me repeating it.

- Inference — vLLM: running the actual model locally so nothing leaves my machine. Slower than an API call, but that was the point.

- Observability — Langfuse: when the agent does something weird, I can actually see why, instead of guessing.

- Tool access — MCP: this is what lets the agent actually touch my email client and file system, instead of just talking about it.

> The journey: I started with a single Python script calling an API — worked fine for a weekend, then broke the moment I needed it to remember anything. Added a memory layer, then realized I had no idea what it was doing half the time, so observability came next. Every piece got added because something broke, not because a tutorial told me to. That's honestly the real lesson — you don't need the whole stack on day one. You need the piece that fixes what's actually failing in front of you.

I'm a technical founder in Chennai. Over the last few months I built two products that work together, and i'm stuck with going forward with it

1. An AI video studio. Similar space to higgsfield, but you see the exact price in rupees before you hit generate, every shot gets a quality score, and you can regenerate just the one bad shot instead of paying for the whole scene again. Works in Tamil, Telugu and Hinglish, not just English. There's also a mode where you shoot real actors on a phone and the AI replaces the entire location with the lighting matched, so a location day that normally costs 50k to a few lakh becomes a few hundred rupees.

2. A free streaming platform for AI films and series. Netflix-style, only AI content.Ratings are head-to-head votes from people who actually finished both films (chess-style Elo, so it can't be gamed or bought). Every film shows how it was made and what it cost. The storage/delivery is set up .

Looking for a co-founder (equity, not a salary role) who has actually done marketing, partnerships or growth before, ideally gets the creator economy or Indian entertainment, and finds this space genuinely exciting rather than just "AI hot right now". India preferred, Chennai is a bonus, remote works. DM me if interested

So this happened back in July and I don't think it got enough attention here.

A researcher going by cereblab ran xAI's Grok Build CLI (v0.2.93) through mitmproxy and found something pretty bad. Even in a test where the agent was explicitly told not to touch a specific file, that file still showed up in a background upload — a separate channel entirely from the normal model traffic, sending Git bundles off to an xAI-controlled Google Cloud Storage bucket. Not just the files the agent had opened either — the bundles could carry tracked files and full commit history.

The numbers are what got people's attention. In the test repo, the actual coding task only generated around 192KB of model traffic. Meanwhile that background channel pushed about 5.1GB. Cereblab worked out the ratio at roughly 27,800x more data leaving the machine than the task ever needed. And because it's grabbing full Git history, not just your current files, if you'd ever committed a secret and later deleted it, that secret could still be sitting in the history that got bundled up and shipped out.

Worth being precise about what this is and isn't: it's not "data exfiltration" in the sense of an attacker stealing something. This was functionality built and operated by xAI itself, running by default. Also notable — disabling the "improve the model" setting didn't stop it. It was a completely separate pathway.

xAI's response was quiet. No security advisory. Independent testing found the server started returning a flag (`disable_codebase_upload: true`) about a day later, which stopped the behavior — but this was a server-side change, not a new client build. Musk said on X that previously uploaded data would be deleted, though there's no independent way to confirm every copy actually was.

Then on July 15, xAI open-sourced the whole thing under Apache 2.0.

Here's where I want to be careful, because my first draft of this post overstated it: it's confirmed that the upload capability existed and that xAI's fix was a remote flag rather than a code change. What's less clear is whether that exact upload logic is still present, unmodified, in the version they later open-sourced — xAI says the release is meant to be runnable locally and is periodically synced from their internal repo, so I can't say for certain the capability is sitting there waiting to be flipped back on without someone doing an actual code-level diff. So take that specific claim as "the activation was controlled remotely, not through the client" rather than "the harmful code is definitely still lurking in there."

Even with that caveat, I think the core point holds: publishing source code makes the client auditable, but it doesn't tell you what a remotely-controlled backend will do, what it retains, or whether server-side behavior can change without anyone seeing a diff. "Open source" here didn't really restore the trust it looks like it should.

Sources if you want to check this yourself:

- Researcher's reproduction/write-up: github.com/cereblab/grok-build-exfil-repro

- xAI's open-source announcement: x.ai/news/grok-build-open-source

- The released source itself: github.com/xai-org/grok-build

Got tired of paying for tools that have a free, open-source twin doing 90% of the job. Spent the last few months testing a bunch of them across notes, productivity, media, and admin/sysadmin stuff. Some were mid, some I uninstalled the same day, but this batch earned a permanent spot on my machine. Sharing in case it saves someone else the trial-and-error.

Notes & docs

Obsidian — swap for Notion or Evernote. Local-first notes, everything's markdown, your files never leave your drive.

Joplin — lighter alternative to Evernote. Syncs painlessly and there's zero lock-in if you ever want out.

LibreOffice — replaces MS Office for most people. Handles docs and sheets fine, no subscription nonsense.

Standard Notes — simple, end-to-end encrypted note app if Apple Notes feels too locked-in.

Productivity & project management

Focalboard — Trello alternative. Kanban boards, self-hostable, feels familiar if you've used Trello before.

Vikunja — to-do and task management, good middle ground between simple checklists and full project tracking.

Cryptpad — Google Docs/Sheets replacement with real end-to-end encryption baked in, works fine for collaborative editing.

AppFlowy — another Notion-style workspace, still maturing but solid for notes plus light project tracking.

Rocket.Chat — Slack alternative, self-hosted team chat with channels, threads, integrations.

Media & files

GIMP — stands in for Photoshop. Learning curve is real but it's way more capable than people give it credit for.

Audacity — does what Adobe Audition does for basic audio editing. Clean exports, no bloat.

Syncthing — cuts out Dropbox and Google Drive. Peer-to-peer sync, no cloud company sitting in the middle.

Jellyfin — Plex without the constant premium-tier nagging. Runs your own media server, your rules.

Security & admin tools

Bitwarden — LastPass alternative. Self-hostable if you want full control over where your passwords live.

KeePassXC — fully offline password manager, no cloud dependency at all, good if 1Password's model bugs you.

Portainer — makes managing Docker containers actually bearable through a clean web UI, no more memorizing CLI flags.

Netdata — real-time server monitoring, install it once and it just quietly watches your infra for you.

Pi-hole — network-wide ad blocking at the DNS level, run it on anything from a Raspberry Pi to a home server.

Uptime Kuma — self-hosted uptime monitor, replaces paid services like UptimeRobot for checking if your stuff is alive.

Wazuh — open-source security monitoring and threat detection if you're managing more than a couple machines.

None of these are perfect drop-in replacements. You'll hit friction somewhere, usually rough UI or a missing plugin here and there. But the tradeoff is you're not stuck in someone else's pricing model, and most of these projects have communities that actually respond when you open an issue.

If you're already running something not on this list, or have a better pick for one of these, drop it below. Always trying to trim this down further.

So i went down a rabbit hole trying to de-google my life a bit and ended up finding some genuinely solid free tools.

Nothing crazy, just stuff that works and doesn't nag you to upgrade every five seconds.

Syncthing — syncs files between your own devices, no cloud, no company in the middle. set it up once and forget it exists.

GIMP — does like 90% of what photoshop does and it's free forever.

Handbrake — shrinks huge video files down without wrecking quality.

now here's a few that need you to open a terminal, still easy though:

Uptime Kuma — self-hosted status page, tells you when your stuff goes down. one docker command and you're live:

```

docker run -d --restart=always -p 3001:3001 -v uptime-kuma:/app/data --name uptime-kuma louislam/uptime-kuma:1

```

then just go to `localhost:3001` in your browser and set it up.

n8n — automation tool, kinda like zapier but self-hosted:

```

docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n

```

Vaultwarden — lightweight bitwarden server if you want your passwords synced across devices but still self-hosted:

```

docker run -d --name vaultwarden -v /vw-data/:/data/ -p 8080:80 vaultwarden/vaultwarden:latest

```

honestly if you've got docker installed already, most of these are copy-paste, hit enter, done. that's the whole barrier.

my approach was just: pick the one thing that's annoying you right now, install that single app, actually use it on a real task same day.

I’m an Indian founder looking for an Indian technical co-founder to build an iOS app with me.

I’m also open to working with someone on a freelance/fixed-fee basis if you’re not looking for a co-founder opportunity.

I’ll be completely transparent: the fixed fee I can offer right now is quite low. I’m not going to lie about that. I’m working with a very tight early-stage budget.

The good part is that the app itself is extremely simple to build.

The idea is also… very weird.
Not technically complicated. Not another AI app. Not a massive social network with 50 features.
Just a very simple product built around a strange idea that could either be ridiculously successful or completely flop.

I genuinely don’t see much middle ground with this one.
It could absolutely blow up and generate serious revenue, or crash completely.

That’s the bet.

I already have the product concept, UI/UX direction, branding and business side figured out. I mainly need someone who can turn the idea into a polished iOS app without over-engineering it.

Looking for someone who:

🇮🇳 Is based in India
Knows Swift / SwiftUI well
Has shipped iOS apps before
Can build quickly
Has good product sense
Doesn’t turn a simple app into a 6-month engineering project
Is interested in either joining as a co-founder or taking on the initial build as a freelancer

I’m intentionally not explaining the idea publicly because the weirdness is kind of the point.

If you’re curious, DM me with:
Your experience
Apps you’ve built
Whether you’d prefer co-founder or freelance
Your expected fee if freelancing
I’ll explain the idea privately.

Hi everyone,

I am a 17 year old solo founder based in the UK. I am currently building an AI education startup to combat the decline in cognative ability caused by LLMs and generic answers. I am a commercial founder so looking for someone on the technical side preferably under 25 to come on and help to bring this product to life. I already have market data that the AI world is moving into more strategic vertical models and i want to be first. So if your young, driven and want to actually build a company that could change lives then drop me a DM. Looking forward to connecting with likeminded entrepreneurs and builders.

Hello all!

I have an ambitious idea, already 99% solid and I’d love a technical person to make it happen.

I’m non-technical and more focused towards product vision.

I’ll be doing anything and everything else besides the technical part.
I’m in need of a person that has good knowledge of how AI operates because the reasoning behind the problem is not always easily solvable + knows building apps and computer vision.

My idea is kind of like Spotify, but in another niche, an ecosystem that revolves around a person’s taste.

First time in the startup world, but I’ve had companies before. I leave every collaborator with total independence and freedom to work as they see fit, as long as we can see results!

Edit1: sorry everyone, forgot to mention, since I’m self funding for the first ~3-6 months (depending how well the app goes) money is “lean” so it would be more worth it for the professionals outside the US!

if you’re in the US we can chat! But come in already saying how much you expect to earn (cash+equity) so we don’t need to have a back and forth!

Edit2: didn’t realize my DMs would get blown, I’m currently in the airport and this week is will be a little hectic on my end.
So if I take sometime to respond, sorry in advance!!

[removed]

I’m a founder building a venture studio, and I have a ton of MVPs that have already shown real demand, solving problems where there’s a clear gap in the market.

The ideas range from FinTech, CleanTech, Real Estate, AI, SaaS, and more.

I’m looking to build a team of people just as crazy as me who want to pick an opportunity, go all-in, and build a company around it.

I’m not looking for an employee. I’m looking for a cofounder.

I bring the ideas, MVPs, technical and product side, and startup-building experience. You bring your expertise, obsession, and willingness to say:

“Fuck it, let’s build.”

If you’re USA or Canada-based, entrepreneurial as hell, and want to build something from 0 → 1, DM me.

Tell me what you do, what you’re ridiculously good at, and what you’ve built before.

Fuck it, let’s build.

​

Hey everyone,

I’m a Graphic Designer currently working with a food brand, and I’m looking to find a technical co-founder to build an AI-powered chatbot/automation product.

I’m a non-technical founder, but I can take care of the marketing, sales, branding, design and business side of the product.

What I’m looking to build

An AI chatbot/automation system that can help businesses with things like:

Customer queries & FAQs

Product/menu information

Lead generation

Taking orders or enquiries

WhatsApp/chat-based customer support

Automating repetitive business workflows

I’m currently exploring n8n and other automation/AI platforms to build the MVP, but I’m looking for someone who can handle the technical architecture, integrations, APIs, AI workflows and development.

Looking for

A technical co-founder who has experience with:

AI/LLMs & chatbots

n8n / Make / Zapier or similar automation tools

APIs & integrations

Backend development

WhatsApp/chatbot integrations

Building and launching MVPs

You don’t necessarily need to be an expert in everything. What matters most is that you can build, experiment and solve technical problems.

What I bring

🎨 Design & branding

📱 Marketing & social media

💰 Sales & customer acquisition

🍔 Experience working with a food brand/business

💡 Product ideas & business understanding

🚀 Willingness to build and test the idea

I’m looking for someone who wants to build this together as a real startup, not just work on a freelance project.

If you’re interested, DM me with a little about yourself, your technical background, and something you’ve built.

Open to discussing equity/co-founder arrangement based on involvement and commitment.

I have been slowly moving my whole stack toward open-source over the past year, partly to save money, partly out of curiosity. Not a purist about it though — some paid tools are just worth it. Figured I'd break down what I've found across the categories I actually use daily, in case it saves someone else the trial and error.

Daily use / productivity -

LibreOffice vs Microsoft 365 — LibreOffice handles 90% of what I need (docs, spreadsheets, basic presentations) with zero cost. Where it falls apart is real-time co-editing and pixel-perfect formatting when a client sends over a heavily designed Word doc. If you collaborate with non-technical people daily, paid 365 still wins.

Thunderbird vs Outlook — Thunderbird's come a long way, calendar and email in one place, works fine. Outlook's edge is really just deep Exchange/Teams integration in corporate environments. Personal use, Thunderbird's fine.

Joplin/Obsidian (free tier) vs Notion — Obsidian's free tier is genuinely generous, local-first, plugin ecosystem is huge. Notion's paid plan wins if you need shared team databases and easy non-technical onboarding.

Admin, server, and database side -

PostgreSQL vs a managed paid DB (RDS, PlanetScale, etc.) — Postgres itself is free and honestly better than most paid engines feature-wise. What you're paying for with managed services is not having to deal with backups, failover, and 3am pages. If you've got the ops skill or time, self-hosted Postgres wins outright.

Proxmox vs VMware — Proxmox has basically caught up for home lab and small business use. VMware's paid licensing still edges ahead for large enterprise clustering and support SLAs, but for most of us that's overkill.

pgAdmin/Adminer vs paid DB GUIs (DataGrip, TablePlus) — free tools cover querying and basic management fine. Paid GUIs are noticeably faster and nicer for complex schema work across multiple DB types, so if you're a full-time DBA it's worth it.

Portainer (free) vs paid container management platforms — Portainer's free tier is enough for most self-hosters. Paid platforms start making sense once you're managing dozens of nodes with RBAC requirements.

AI-based tools -

Ollama (running open models locally) vs ChatGPT/Claude paid tiers — local models have gotten surprisingly usable for coding help, summarizing, and drafting, and you get full privacy plus zero API cost. But for genuinely hard reasoning, long context, or the newest capabilities, paid hosted models are still ahead by a real margin.

Stable Diffusion (self-hosted) vs Midjourney — Stable Diffusion gives you full control and no per-image cost once you've got the hardware, but Midjourney's out-of-the-box image quality and prompt handling is still smoother for most people.

Whisper (open source) vs paid transcription APIs — Whisper is basically as accurate as most paid transcription services now. Paid ones mainly win on speaker diarization and turnaround speed at scale.

Educational apps -

Anki vs paid spaced-repetition apps — Anki free is legitimately one of the best study tools that exists, full stop. Paid alternatives mostly just polish the UI.

Khan Academy / Moodle vs paid LMS platforms (Canvas, etc.) — Moodle is powerful but takes real setup effort. Paid LMS platforms win on ease of onboarding for schools that don't have IT staff to maintain it.

Jupyter/Anaconda vs paid coding education platforms — free tools are all you need to actually learn, paid platforms are paying for the packaged curriculum and hand-holding, not the tooling itself.

Overall takeaway:

The pattern that keeps showing up: open-source usually wins on raw capability and cost, paid usually wins on time saved, support, and polish for non-technical users. If you've got the patience to configure things yourself, you can build a fully capable stack for free across almost every category above. If your time is worth more than the subscription cost, paid still makes sense in specific spots — mainly managed infra and cutting-edge AI.

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

Just found this track and it's exactly the "put your phone down for 3 minutes" energy we all need after a heavy week online. You must go to YouTube and check the comments lol!!

I'm looking for a co-founder partner, and I'm taking a more natural approach than what I see going on here.

My proposal: Let's start working on a project (new or existing) and see if we're compatible. Honestly, that tells us more about each other than anything else. If the collaboration fits, we can form a legal business partnership.

About me: I'm US-based, what I bring to the table is highly technical background and software engineering skills (not just for show or internet lingo, but I actually live and breathe my tech.) If you want to know more about me, I'm happy to share in DMs.

Minimum requirements so we don't waste each other's time.

-You're an adult.

-You have at least 20+ hours a week available to dedicate to a project with me.

-You communicate with me daily, even if it's just to say "hi".

-You don't have all your conversations with me using only ChatGPT (I'm not against AI-usage when it's appropriate)

-You're not looking for me to send you money.

-You are able to be honest and say what is on your mind if you need to.

-You're ok being part of a very small, most likely 2-person team.

-You can be part of logically resolving a disagreement.

I believe my requirements have narrowed the pool down enough to avoid people who aren't serious. Send me a message if you're interested in my offer. This is not a "Paid Role" it would be a form of equity that we discuss. Do not leave a comment that says "dm me" or I'll know you didn't even read this.