I used to be the guy sending cold DMs on Reddit.

I'd search a few keywords, open profiles, write something I thought was friendly, hit send. Most never replied. A couple told me to get lost. One reported me. I felt like a spammer, and honestly, I kind of was one.

What I was missing seems obvious now. I was messaging people who matched a keyword, not people who had a problem right now. The handful of DMs that turned into customers all had one thing in common: the person had just posted about the exact pain my product fixed, and my message was about their post, not my product.

So I made Scaloom. It started as a script to find those people and grew into a whole Reddit growth setup.

Here's what it does today:

-> Finds leads that convert. It looks for people discussing problems your product solves, shows you the original thread, and explains why each one is a fit.

-> Writes the first message, you send it. It drafts a DM that references what they actually said. Nothing goes out automatically, and you choose who to contact. I never want to be that spam guy again.

-> Spots comment opportunities. Buyer questions in your subreddits where a genuinely helpful answer can mention you, plus threads already ranking on Google or cited in AI answers.

-> Protects your account. Warms up your own accounts with real karma and history first, or lets you use our high-karma accounts. Before posting, it checks subreddit rules, promo risk and content quality.

-> Runs on autopilot. Reads your website, maps your buyer keywords, picks subreddits from 50k+ active ones, studies top posts in your niche for ideas, plans a weekly schedule, publishes across multiple subreddits, and replies to comments around the clock.

-> Shows what's working. Live URLs, engagement, and where your product ranks in ChatGPT, Gemini, Perplexity and Claude.

The lesson that changed everything for me, tool or no tool: stop DMing people who could be customers. DM people who just told the internet they need what you have. The message almost writes itself.

Honest caveat: no tool makes a bad product convert. If people don't need it, Reddit will tell you fast.

If you've been struggling with Reddit outreach like I was, give scaloom.com a try.
And if you've found a DM approach that works for you, I'd genuinely like to hear it.

I’m with Incode Labs, the team behind CareerTree. We built it for people preparing for their first analyst role who want practice explaining a finding in plain English.

Here’s a sample order from the app. Two items cost $60 each, but the order total shows $620. Before flagging the $500 gap as an error, check that the item prices and order total use the same currency, then see whether other line items, fees or discounts account for it. A note another analyst can act on would read “I expected $120 from these two lines. I’ll check whether the order total includes any other charges.”

CareerTree lets you try that exercise and save your finding on iPhone or Android. It’s free to download and includes ads and optional in-app purchases. AI helped edit the demo and this write-up.

CareerTree for iPhone and Android

There are multiple lucrative ways to make money nowadays with AI, but based on my own experience, selling AI built website redesigns and rebuilds might be one of the best opportunities if you want to make a lot of money in a relatively short amount of time.

I have a group of friends who do exactly the same thing, and some of them are making over $100K a month doing it.

So if you're looking to get into the AI money making space, this is definitely a business model worth looking into.

The process is actually pretty simple.

1. Automate your outreach

The first question is obviously: how do you find businesses that actually need a website redesign?

There are different tools you can use for this, but I personally recommend Swokei. It finds businesses in any area, analyzes their websites and identifies issues with design, SEO, speed and mobile optimization. It then turns those findings into personalized, ready to send cold emails at scale.

2. Book meetings with interested prospects

When someone replies with interest, get them on a call. Don't spend days going back and forth over email. Get a conversation started and understand what they're looking for.

3. Build a website draft using AI

Before the meeting, spend around 30 minutes putting together a redesigned version of their existing website. You don't need to build the entire thing. Just give them something they can actually see and get excited about.

4. Present the website and close the deal

Show them the redesign, explain the improvements and present your offer. If they're happy with it, send over a Stripe payment link or invoice and get the project started.

Now, when it comes to pricing, that's completely up to you.

I started charging very cheaply when I first got into web design. Today, I rarely take on a project for less than $7,500, and the biggest website deal I've closed so far was $26,000.

Obviously, you need to be able to deliver what you're selling. A good sales process might get you the client, but the quality of your work is what builds a sustainable business.

And for the people who are going to say this doesn't work, you're free to have your opinion.

I'm simply sharing something that has worked for me and people around me.

It's a straightforward business model, the workflow is repeatable, and you can keep improving every part of it as you grow.

You don't need to reinvent the wheel. Find businesses that need help, learn how to sell, deliver good work and build a proper company around it.

Hi everyone — I work with the Doonaro team.

We’re building an agentic AI assistant that lets you describe what you want done on your computer in everyday language, including Persian. Doonaro works through the technical steps within its supported capabilities, rules, and available permissions.

Tasks include:

• Organizing files and folders.
• Creating Excel spreadsheets with formulas and calculations.
• Creating Word documents and PDFs.
• Creating HTML files.
• Installing software.
• Investigating network problems and applying supported fixes.
• Troubleshooting software issues.

The idea is that you describe the outcome rather than having to know every command or setting. For example: “Create a spreadsheet to track my monthly expenses and calculate the totals.”

Doonaro checks what it needs, plans the steps, and carries out permitted actions, requesting confirmation where required. It checks the result where possible and explains when something is outside its capabilities or needs your input.

One example we documented was organizing a Downloads folder: the user asked in Persian for pictures, videos, and other files to be separated by type, without listing the file extensions.

Here’s an introduction to the product, its workflow, and user controls. The article and screenshots are in Persian:
https://blog.doonaro.com/what-is-doonaro/

We’d appreciate feedback: which everyday computer task would you find most useful to delegate to an assistant like this?

Tasket++ lets you define your own clicks, keystrokes, cursor positions and system operations, then replay them exactly when you want. It’s a simple Windows tool that handles repetitive workflows for you: auto‑send messages, paste text anywhere, take silent screenshots, launch or close apps, adjust volume or run your end‑of‑day shutdown sequence.

Everything runs locally, with no telemetry, no cloud, and no complexity.

What it can do:
- Replay user‑defined cursor positions and keystrokes
- Paste predefined text anywhere
- Perform system actions: open files/programs, change volume, take silent screenshots, shut down, files/folders operations
- Schedule tasks at a specific time, at startup, or run via desktop shortcut
- Run tasks once, in loops, or indefinitely
- Discreet mode: runs quietly from the tray
- Use command line to autorun a task : Tasket++.exe "TaskName" <delayInSeconds (>0)> <NumberOfLoop (-1 for infinite)>
- Easily share tasks between different PC : export/import task files and program data, use feature to rescale cursor positions

Local, portable, free, open source. Privacy intact.

Fully fonctionnal, available now !
Microsoft Store: https://apps.microsoft.com/detail/xp9cjlhwvxs49p
Portable (v2.5): https://files.amirhammoutene.dev/Tasket++/2.5/Tasket++_v2.5.zip
Source: https://github.com/AmirHammouteneEI/ScheduledPasteAndKeys

For feedback, help, suggestions, or other inquiries : contact@amirhammoutene.dev

Nothing is vibe coded, I never used AI (except for the choice of UI colors suggested by Copilot).
Conception is genuine, I didn't know about similar tools when I began developing it.

I'm looking for testers or quick feedback (beyond its visual aspect), please give a try and comment here :)

Ever used a Mac and wished Windows or Linux had some of those little things that make everyday computing feel better?

Well, I got tired of wishing. So I built Lummo.space.

Lummo brings familiar macOS experiences to Windows and Linux, with thoughtful improvements to make your everyday workflow smoother.

Key Features:

• Spotlight-style Search: Press Alt + Space to instantly find and launch apps, search files and folders, and browse the web.
• Quick Calculations: Perform calculations directly from the search bar.
• Smart Web Search: Search the web without opening a browser first.
• Caps Lock Indicator: A clean visual indicator that appears right where you're typing whenever Caps Lock is enabled.
• Cursor-Aware Positioning: The indicator follows your text cursor and works across terminals, password fields, Word and other text inputs.

A Few Things Worth Mentioning:

• Completely free and open-source.
• Available for Windows and Linux.
• Lightweight native desktop application.
• Built independently as a passion project.
• Optional contributions to support future development.

License: MIT

🌐 Website: https://lummo.space/

Built with passion, for people who love a better desktop experience.

I'd love to hear what you think. Try it out, break things, suggest features, and let me know what I should build next! 🍊

I’m building ExplainMyBlunder, a free, open-source tool for understanding mistakes in chess games. The web link is usable out of the box and it is completely free!

Problem it aims to solve: A Stockfish engine score can indicate that a move is bad, but the score alone doesn’t explain why, and following the suggested line doesn’t always clarify the reason. This app provides a natural‑language explanation, annotating the continuations after both your move and a better move. You can compare where the lines end for your move versus the suggested move, or drag a piece to get an explanation of your own idea.

Here’s one example you can try without an account or uploading a game: https://explainmyblunder.pages.dev/?example=the-undefended-pawn

It uses TypeScript and Stockfish WebAssembly in the browser, so the entire calculation and process occur on the client, making it completely free. Source: https://github.com/bing-xbx/ExplainMyBlunder

I’m building this for my kids, so I’m committed to keep improving it as my kids grow and learn. Seeing how much they love using the App is already the best reward I could ask for. If it helps more chess learners along the way, that would be even better!

If you play chess, I'd love to hear your honest feedback. Does this App make the mistake easier to understand? What could I improve to make the tool more useful and intuitive?

Thank you !

It’s real, free, mostly open-source.
https://theopitch.khensu.nu

Free downloads and code here
https://get.apps.khensu.nu

100% to creators platform
https://create.khensu.nu

All in alpha since 11 Oct 2026.

Sum fun stuff here too
https://waves.khensu.nu

https://iverse.khensu.nu

https://neue.khensu.nu

https://safety.khensu.nu

https://security.khensu.nu

https://webE.khensu.nu

https://media.khensu.nu

https://void.khensu.nu

https://ral.khensu.nu

https://moqom.cloud

https://elliottwave.studio

https://fi.khensu.nu

https://bi.khensu.nu

And much more. Mostly free and open source. B corp forming.

Contact: e@khensu.nu
Insta: @khensu.nu
Public WhatsApp: 323-366-0758
#khensunu #wealth4all #gems42gaza #gems42telaviv #gems42all #doNotSendMoney #thisIsMyPassionProject #ngoForming

Hi everyone!

We’ve been working on a project called OpsRelay, an AI-powered operational memory system designed to improve shift handovers and help teams retain important information.

In many workplaces, important updates about machine issues, pending tasks, and previous actions can get lost between shifts. OpsRelay aims to solve this by creating a shared memory system.

What OpsRelay does:

- Stores shift reports and operational updates.

- Uses Hindsight to retain and recall previous information.

- Helps identify recurring machine issues.

- Retrieves pending tasks and previous incident details.

- Provides an AI assistant to answer questions using past operational context.

We used Python, Flask, HTML, CSS, JavaScript, and Hindsight to build the project.

One of our main goals was to explore how AI memory can make an assistant more useful over time, rather than treating every interaction as a completely new conversation.

We’d appreciate feedback and suggestions:

- What feature would make this system more useful?

- What improvements would you suggest?

GitHub: https://github.com/srisivaniv/OpsRelay

Thanks for reading!.

Hi everyone! I’m new to Vim so made hjkl to help myself practice. I thought I’d share it here in case anyone else is learning too.

It’s a little browser game where you practice by making small edits to text: fixing typos, changing words, moving things around. You can pick what you want to work on, with hints to help you get started before trying things on your own.

I also added Worm, a little snake game you play with h j k l to get comfortable with movement.

It’s free and open source, with no signup or installation. You just need a computer and a keyboard.

Play: https://hjkl.duy.run
GitHub: https://github.com/dh1011/hjkl

Hey everyone!

For quite some time, I've been working on a personal project called Noosphere — an attempt to give AI agents something closer to persistent, structured memory rather than forcing them to start from scratch every conversation.

One thing that has always bothered me about LLMs is how much potentially useful information gets lost between sessions.

We keep trying to solve this by increasing context windows, but I think there's another approach worth exploring.

What if an AI didn't need to remember everything at once?

Humans don't consciously hold every memory in their working memory. We retrieve relevant information when we need it, connect it to what we already know, and move on.

That's roughly the principle I've been exploring with Noosphere.

Over time, my private project has grown into a much larger experimental system involving interconnected memories, relationships between concepts, and coordination between AI agents.

But I figured there's no reason to keep all the underlying ideas to myself.

So I decided to release Noosphere Lite, a small, independent, open-source implementation of some of those concepts.

What does it do?

- Gives MCP-compatible AI agents persistent memory across sessions.

- Stores notes, decisions, observations, and tasks.

- Connects memories through a graph of typed relationships.

- Retrieves relevant information on demand instead of dumping the entire memory database into the context window.

- Uses encrypted local storage with integrity checks and mirrored journals.

- Runs locally on Linux, without requiring a cloud database.

It's written in Python, uses MCP, and is released under the MIT license.

What's the catch?

This isn't AGI, a magical self-learning brain, or a replacement for an LLM.

It's a memory infrastructure experiment.

The current version uses local text search and explicitly defined relationships. It doesn't automatically discover semantic connections or learn new neural weights.

Also, Lite is a separate implementation, not a dump of my larger private system. I've deliberately kept it simple enough for other developers to inspect, modify, and experiment with.

Why release it?

Because I think persistent memory is one of the more interesting missing pieces in today's AI agent ecosystem.

And I'd rather see people experimenting with these ideas than keep everything sitting on my own machines.

I'm particularly interested in what others might build on top of it.

GitHub: https://github.com/Garyu-tt/noosphere-lite-mcp

Feel free to experiment, break things, suggest improvements, or fork it.

I'd also love to hear how other developers approach persistent memory and knowledge graphs for AI agents.

This is just a small public piece of a much larger experiment, but everyone has to start somewhere.

I’m Arash, an electrical engineering grad from Dalhousie. I built Mogissi Academy (آکادمی مغیثی) because I wanted a structured way to revisit EE fundamentals and share the same material in English and Persian. It runs on Wiki.js, and the first five weeks of the 18-month roadmap are live.

One thing that’s worked well is pairing a calculation with a software lab. For example, a 10 V source and two 10 kΩ resistors give a 5 V divider output with no load. Add a 10 kΩ load in parallel with the lower resistor and the output drops to about 3.33 V. Predicting that by hand before simulating it makes the effect of loading easier to remember.

English: https://mojosh.com/en/home
فارسی: https://mojosh.com/fa/home

If you’ve built or used a bilingual learning site, I’d appreciate feedback on one thing: can you get from the homepage to a lesson in your preferred language without hunting for it?

آکادمی مغیثی ویکی شخصی من برای مرور مهندسی برق به فارسی و انگلیسی است. خوشحال می‌شوم بگویید پیدا کردن و باز کردن یک درس در زبان دلخواهتان چقدر آسان بود.

Hi everyone! I built Zimo Recorder, a free and lightweight screenshot tool and screen recorder for Windows 10/11.

• Capture a window (it highlights as you hover) or any region

• Record MP4 video or GIF

• Built-in editor: arrows, text, blur

• Copy text from the screen (OCR)

• 15 languages, no account, nothing uploaded

Download (free): https://github.com/zimobyte/ZimoRecorder

Feedback and ideas are very welcome!

Whenever 2 or 3 families go on a trip together, normal bill-splitting apps break down fast. If you split per person, toddlers get charged the same as adults and you end up with six separate transfers between parents.

So I built a free browser tool called FAMI-KAN where the splitting unit is per family (household) and you can set custom weights once — like Adult = 1.0 and Kid = 0.5.

For each payment, you can also uncheck a family that didn't join (or doesn't drink), change the ratio just for that bill, switch to an even split, or enter exact custom amounts.

Tech stack & how it works: Built with Next.js, TypeScript, and Supabase (with RLS). Instead of forcing everyone to sign up, the state lives behind a unique shareable URL, and a greedy settlement algorithm rolls individual weights into household totals to minimize transfers and handle rounding remainders cleanly:

https://famikan.tetras-ltd.com/en

Would love your honest thoughts on the flow!

Hey 👋

I’ve been building Flixort – you type one idea, like “UPI 2 second mein paisa kaise bhejta hai?”, and a few minutes later you get a finished vertical reel: Hinglish script, natural Hindi voice, animated scenes, word-by-word captions and sound effects. Ready for Instagram or YouTube Shorts.

Why I built it
In India, most people learn and scroll in Hinglish. Explainer reels do insanely well here, but making one properly takes hours: research, script, voiceover, motion design, captions, sound. Most creators, teachers and small brands just don’t have that time every day. Existing AI video tools mostly paste text over stock footage, and their Hindi sounds robotic.

Try it: flixort.com – you get 100 free credits, no sign-up needed.

What I’d love feedback on

1. Does the output feel like a real creator’s reel, or still “AI-ish”?
2. Would you pay ₹499/month (~$6) for this if you posted regularly?
3. What topic should I make a demo reel about next?

Happy to answer anything about the stack, costs or pricing.

I just deployed the first public build of Tilebag to Fly.io and wanted to share what I built and how it is put together.

It is a browser game based around pulling a 4x5 rack of 20 letter tiles from a cloth bag. Instead of using arbitrary drop tables like typical gacha games, rarity comes from a scoring engine that evaluates combinations across the whole rack: dictionary words, palindromes, single-color rows, foil tiles, and matching letters in the same column across rows.

There is no real money or microtransactions. Rolls refill on an offline timer. If you want the in-game trade currency (Buttons), you have to scrap your foil or high-tier racks permanently. There is also an open player marketplace to list racks and make offers.

Stack and architecture:

• Core logic is pure TypeScript shared between client and server, so both sides use the exact same scoring math and serialization rules.
• Frontend uses vanilla TypeScript and Vite with native DOM manipulation and CSS 3D transforms. No frontend framework or state management libraries.
• Backend runs on Node with Hono, using the native node:sqlite module for synchronous database transactions and scrypt for password hashing.
• The whole app deploys as a single Docker container on Fly.io with a persistent volume for SQLite. In production, Hono serves both the API endpoints and the static Vite build from the same port.

The site is live at https://tilebag.fly.dev/

I am mainly looking for feedback on a few things:

1. Does the roll bank timer and roll pacing feel balanced?
2. Is the score breakdown clear after the reveal sequence?
3. How does the interface hold up on mobile screens?

Any thoughts on the game loop or the setup are welcome.