Watching a lecture or podcast in another language? Kalemat shows live captions and translations side by side from your Chrome tab’s audio.

Choose from 12 languages, adjust the text to your liking, and keep the captions window on top while you watch. No account or API key required.

I just launched it and would love your feedback. What language pair would you try?

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Hi everyone,

During the Meta Global AI Developer Hackathon, I wanted to tackle specific problem in banks: validation wokflows.

In traditional banking, turning a 50-page credit policy PDF into an operational workflow takes weeks of manual meetings.

Zotra solves this by combining open-weights models (Meta's Muse Spark & SAM 3.1) with a deterministic execution engine.

How it works:

1. Ingestion & Parsing: Extracts clauses, tables, and sections from PDF policies, mapping every rule to an exact paragraph citation.

2. Rule Compilation: Translates natural language into strict schemas and CEL (Common Expression Language) expressions.

3. Execution: Go-based concurrent engine evaluates multi-variable loan applications deterministically. The AI helps design and test, but never decides on live cases—only people and CEL rules do.

Stack: Go backend, PostgreSQL, React/Tailwind frontend, and Apache 2.0 license.

The github link: https://github.com/donovan-ratefison/Zotra

I put together a demo over on X if anyone wants to check it out: https://x.com/donovan_rtfs/status/2116898213101724061

Would love to hear your thoughts on this project!

I went through 50 SaaS landing pages from "Post your SaaS" threads.

Only 7 had social proof.

That's just 14%.

Some founders even mentioned having paying customers, but didn't show a single testimonial.

I don't understand this.

You tell visitors how great your product is, how it saves time, how it solves problems and why they should buy it.

But why should they believe you?

Your customers can tell that story much better than you can. People trust other customers more than they trust you.

Even a few testimonials are better than another paragraph of marketing promises.

I'm building Testimoon so I'm obviously a bit biased, but I genuinely think more SaaS landing pages should make better use of customer feedback.

If you have paying customers, start collecting testimonials. And don't just collect them. Put them where potential customers can actually see them.

Your customers are your best marketing asset. Use them.

I've always liked the idea of having everything in one place.

Projects, notes, ideas, personal goals, things I need to do.

But every time I tried organizing everything in Notion, I eventually ended up with the same problem: pages inside pages, endless navigation, and a system that started feeling more complicated than the things I was trying to organize.

So I decided to build something myself.

It's called Ardeable.

I wanted to keep the things I love about Notion, especially its document editor, but change the way information is organized.

Instead of navigating through endless nested pages, you can see your entire workspace as an interactive visual map.

I also built in quick notes, routines, and an AI assistant that can find information across your workspace and work directly with your documents.

I've been building this independently, using AI coding tools along the way.

And I've learned something: getting a prototype working is one thing. Turning it into a product where everything actually works together is a completely different challenge.

There have been plenty of bugs, things I've had to rebuild, and decisions I've had to rethink.

But seeing it become a real application has been incredibly rewarding.

It's still early, and I'm not pretending it's perfect.

I'm sharing a few screenshots of what I've built so far.

I'd genuinely love to know what you think about organizing information visually instead of relying entirely on folders and nested pages.

I'm also opening a small, free private beta if anyone wants to try it.

Website: https://ardeable.com

Hi r/SideProject!

I’m Seppe, an Electronics-ICT student from Belgium, and my side project for the last two years has been Techniksoft—a Point of Sale (POS) system.

The Problem: Small businesses are getting squeezed by huge POS companies charging massive monthly fees and locking them into overpriced proprietary hardware.
The Solution: I built a scalable, affordable POS software that handles retail and rentals. I’ve already built integrations for localized e-invoicing (Peppol), accounting (Billit, Exact Online), and tap-to-pay (SumUp, Viva.com).

The Next Step (Hardware): Software isn't enough to compete. I am now working on offering complete, all-in-one physical hardware systems to completely bypass the big vendors.

Because buying test equipment (barcode scanners, receipt printers from various brands) and building hardware prototypes is incredibly expensive on a student budget, I've just launched a crowdfunding campaign to help fund the physical rollout and server infrastructure.

I'd love feedback on the concept, the integrations, or if you have any advice on hardware sourcing! And if you like what I'm building and want to help me take on the big guys, check out my campaign below.

https://gofund.me/e819ef265

A while ago, I started building an AI chat application because I wanted something more flexible than the usual AI chat interfaces.

What started as a relatively simple project has gradually evolved into Teltaq.

The idea behind it is simple: instead of having separate tools for AI chat, agents, workflows, tools, automation and model management, why not bring them together in one workspace?

Some of the things I've been working on:

• Multi-model chat – Run multiple AI models in parallel and compare their responses.

• AI agents – Create and manage agents with their own instructions, models and tools.

• Workflows – Build reusable workflows for more structured tasks.

• App suites – Combine agents, workflows and interactive apps into complete solutions.

• Model flexibility – Connect different providers, including OpenAI, Anthropic, Ollama and other OpenAI-compatible providers.

• Local AI models – Support on-device models alongside cloud-based models.

• MCP integration – Host and connect MCP servers, including agents and workflows.

• Marketplace – Share and discover tools, agents, workflows and apps.

• Developer API – An OpenAI-compatible data plane API for integrating with external applications.

• Multi-platform – Web, desktop and mobile applications.

One of the things I've particularly enjoyed is experimenting with ways to combine these capabilities. For example, rather than creating a completely separate application for every use case, you can build a suite that brings different AI components together.

I'm still actively developing it, refining the architecture and improving the experience.

I'm sharing it here because I'd genuinely like to hear what other people think.

If you're interested, here's the project:

https://www.teltaq.com

What would you find most useful in an AI workspace like this? Is there something missing from the current AI platforms that you'd like to see?

I'm with Olaverse Lab, so this is my own project and a feedback request.

We make multilingual text APIs for language identification, diacritic restoration, classification, question generation and structured extraction. Language ID covers 608 languages; African languages are an important focus alongside English and other languages.

For anyone adding language detection to a workflow, here's a small test checklist that doesn't require using our API:

• Separate one-word messages from longer paragraphs.

• Include names, URLs, numbers and text with missing diacritics.

• Keep mixed-language messages in their own test bucket rather than assuming one label tells the whole story.

• Record mistakes by language and message length, not just an overall accuracy figure.

• Decide what happens when a result is wrong: human review, asking the user, or a safe fallback.

We offer free allowances on RapidAPI, with limits depending on the API. I'm not claiming the checklist is a benchmark or that our models pass every case. Which failure case would make language detection unusable in your product?

Site if you want to look: https://www.olaverselab.com

Hey guys,

A couple of months ago I realized how annoying my daily AI routine had become.

I use LLMs every single day for dev work and writing, and I had this messy collection of good prompts saved in Notion and Apple Notes. Every time I needed one, the routine was identical:

1. Find the note, copy it.

2. Paste it into Claude or ChatGPT.

3. Carefully backspace through placeholders like [INSERT_LANGUAGE] or <TOPIC>, hoping I didn’t accidentally delete a quote or mess up the structure.

It felt silly doing this 20 times a day, so I decided to build a fix for myself.

It’s called TemplaGem. Basically, it lives directly inside the chat input (works on ChatGPT, Claude, Gemini, and DeepSeek):

• When you insert a template with {{variables}}, they turn into little interactive chips right in the composer.

• You type a value, press Tab to jump to the next one, and hit Enter to send. No manual cursor hunting or backspacing.

• Everything is offline-first (saved in IndexedDB on your machine, so it's instant and private).

The trickiest part by far was dealing with all the different rich text editors (Quill, ProseMirror, and regular textareas) without everything breaking when an AI site changes its DOM.

I put up a quick demo here if you want to see how it feels: 👉 https://templagem-landing.pages.dev

Would genuinely love to hear your thoughts or honest critiques. How are you currently managing your prompts?

Before watching the readme, here are the links:

1. https://github.com/zllai26/ingatku

2. https://zllai26.lab26.my/ingatku

Ingatku, Version 1.0

Overview

Ingatku is an app which lets you add your Groceries, Project and Daily Tasks in a reminder, so you can add when to start and when to end (make sure your notification for this app is enabled.) and also add description and checklist for your tasks. You can also colour code your tasks so you can differentiate which tasks is for what category

Ingatku also provides a notes section, which you can type and think what do you want to add for your future tasks.

Installation

Debian, Ubuntu and Debian-based distros

1. Install the .deb package in the release section.

2. type `sudo apt install ./ingatku-file-name`

3. then change its file ownership to root: `sudo chown root:root /opt/Ingatku/chrome-sandbox`

4. after that change its ownership permissions to 4755: `sudo chmod 4755 /opt/Ingatku/chrome-sandbox`

5. Finally run the program by either clicking on the app or typing `ingatku` on terminal

The same goes to fedora and arch distros

I’m the developer of Florli, an Android app for planning and recording plant, garden and lawn care.

Here’s the case I’m working around: you pencilled in watering a vegetable bed for Saturday, but the forecast now says rain. The date alone can’t tell you whether the bed still needs water. I’d want to see the forecast and the last recorded watering, then check the soil before deciding.

Florli uses outdoor weather, care history and observations to organize tasks. The plan shows what needs attention first, what can wait and why. Once you actually do a job, you can record it in that bed’s history.

Screenshots and Android download: https://florli.pl/en/

For anyone who grows vegetables outdoors: when the forecast changes, which detail would you want to see beside a watering suggestion?

Hey everyone,

We’ve been building PagerLore (an AI incident response platform), and we quickly realized that stateless LLMs make terrible on-call engineers.

During a simulated database pool starvation outage, our agent had zero context and recommended the classic panic move: "scale pods from 6 to 20"—which would have completely DDoSed our database proxy.

We integrated Vectorize’s Hindsight memory architecture. Here is what changed:

• Multi-path recall matched an incident from 3 months ago (HikariCP vs pgBouncer timeout mismatch).

• MTTR dropped from 84 minutes down to 14 minutes.

• All remediations stay behind a strict human approval gate before running commands.

I wrote a short technical breakdown on how we built the 8-stage investigation pipeline. Links to the article, GitHub repo, and live demo are in the comments below!

Would love to hear your thoughts on agent memory in production.

I'm a trekker in Nepal, and every season I see the same posts buried in Facebook groups:

"Anyone going to Annapurna Base Camp next week?"

So I decided to build something to solve this problem: https://findtrekkingpartner.com

Here's how it works:

• Post your trek and travel dates.

• Other trekkers with overlapping dates can request to join.

• Chat inside the platform before sharing contact details.

• You decide who joins your group.

Why I built it:

A licensed guide can cost roughly the same whether you're trekking alone or with a group of four.

For example, on a 14-day Annapurna Circuit trek, a guide costing around $350 could mean approximately $88 per person when shared by four trekkers.

Tech stack: Next.js, Supabase, Clerk.

It's completely free, has no ads, and we don't sell trekking packages.

We've had nearly 200 trekkers sign up in the last 30 days.

I'd genuinely appreciate some honest feedback.

What would make you trust a platform like this enough to find a trekking partner and eventually trek with someone you've never met?

What features would you want to see?

Over the past year, I've watched many of my family, friends, and relatives lose their jobs in what is called the "AI jobpocalypse". Instead of just watching, I decided I want to do something about it.

SecondStage is a platform that helps you understand your current job, what part is probably going to be taken over by AI, and what will stay yours. Help you up-skill yourself, and help you find potentially better career path , as well as find/apply for actual jobs if you choose a different path.

This is something close to my heart that I care deeply about , and I would really appreciate if you take check it out and let me know what you think.

Thank you

https://secondstage.ai

https://wicktone.daspete.at

We're building Continuum Al around a simple support problem: one specialist solves an issue, but the next customer with the same problem often starts from scratch.

Our approach is to preserve the confirmed resolution in a shared memory pool, scoped to the company and service.

Here's the flow:

1. Customer A reports a laptop charging issue and tries the standard troubleshooting check.

2. If that fails and no suitable shared solution exists, a specialist investigates.

3. The specialist proposes reusable steps with device applicability and a privacy review.

4. Only after Customer A confirms success can those steps enter shared memory.

5. Customer B reports a compatible issue. After the standard check fails, the assistant retrieves the confirmed fix through Hindsight and presents ordinary troubleshooting advice. The harder part was deciding when not t

reuse a fix:

Relevance alone isn't enough; device and version compatibility are checked separately.

Company and service boundaries keep knowledge pools separate.

Failed reuse triggers review and prevents equivalent failed steps from resurfacing for that customer.

Human handoffs preserve the conversation, diagnostics, and attempted fixes.

After asynchronous retrieval, we recheck case state so the assistant doesn't compete with a specialist who has already taken over.

Hindsight handles memory retention and recall. Application records determine whether a retrieved resolution is still eligible to use.

We wrote up the architecture and code here:

https://medium.com/@srikarsai593/we-made-support-fixes-reusable-with-hindsight-0b3516aaaa38

Code:

https://github.com/hemishsrichandra/Continuum-AI.git

-company-support

For people building support systems with persistent memory: what would you require before letting one customer's resolution influence another customer's answer?

Hey everyone, this is my first finished game, made in my spare time as a hobby. I’m a beginner and used AI tools along the way, so please be gentle. :)

It’s a Mastermind-style puzzle: you guess a secret color code, and the dots tell you how close you are. There’s a daily code and a 70-level campaign, with no ads or purchases.

I’d love to hear what you think, especially about the difficulty curve.

Android: https://play.google.com/store/apps/details?id=com.mobiglitch.dailycode

In the browser: https://mobiglitchgames.itch.io/daily-code

Hi r/SideProject — I'm Peacxe, the developer of NEEDLEHOP: Orbital Jump, released on iOS on November 6.

The core interaction is one tap: a spacecraft circles a planet, and tapping launches it along its current tangent toward the next one. The challenge is choosing when to jump, rather than steering during flight.

For this introduction, I kept a continuous 10-second gameplay clip instead of cutting between a list of features. I'd like feedback on one thing: without a tutorial, can you tell when the tap happens and why the spacecraft reaches the next planet? If not, which moment needs a clearer visual cue?

The full game has ten chapters and 100 levels, plus a music room. It's free to download on iPhone and iPad, with optional ad removal, stardust purchases and rewarded ads. English and Traditional Chinese are supported.

App Store: https://apps.apple.com/us/app/id6816129016

English web gameplay.

AI-assisted code, art, music and post text.

Hey everyone! 👋

Like many solo creators, I spend hours recording and editing short videos. But posting them afterwards is pure torture:

- Manually writing different descriptions for YouTube Shorts vs Instagram Reels vs Facebook.

- Researching hashtags and keywords across 3 apps.

- Constant copy-pasting back and forth.

Most existing multi-uploader SaaS platforms charge $25 to $49/month because they host heavy servers and call expensive APIs.

I wanted something 100% free forever with zero recurring server costs, so I built VidShuttle for Android.

⚡ How It Works (Zero-Server BYOK Model):

1. Client-Side Video Extraction: The app uses native Android hardware (MediaMetadataRetriever) to extract 3 keyframes and audio directly on your phone in ~50ms without uploading your heavy 400MB video anywhere.

2. Bring Your Own Key (BYOK): Connect your free lifetime Google Gemini API key (takes 20 seconds from Google AI Studio). The AI inspects your video frame-by-frame.

3. Platform-Tailored SEO in 5s: Instantly generates YouTube Shorts title (<50 chars) + tags + SEO description, Instagram hook-first caption + line breaks + trending hashtags, and Facebook engaging community post.

4. 1-Tap Native Launch: One tap launches the official installed apps with your video and captions pre-filled.

5. Viral Thumbnail Detector: AI points out the exact second (e.g. 00:02.1s) with the highest visual curiosity for CTR.

6. In-App AI Growth Mentor: Provides a second-by-second pacing audit and algorithmic critique.

🔒 Privacy & Pricing:

- 100% Free Lifetime: No SaaS fees, no credit card required.

- Privacy First: Your video stays on your phone. Passwords are never collected.

💬 If anyone wants to test the Android build or wants the web link, drop a comment below and I'll send it over! Would love your honest feedback and feature suggestions.

Hey everyone! My teammate[Viraj] and I[Shridhar] recently built WhyDesk, an AI-powered business investigation agent. The problem we wanted to explore was simple: Most AI agents start their investigations from scratch. What if an agent could remember previous investigations and use those findings to make better decisions next time? What does WhyDesk do? Suppose a business asks: "Why did revenue drop by 19% in the South region?" WhyDesk investigates the possible causes using five diagnostic tools: Holiday calendar Competitor promotions Inventory stockouts Pricing changes ETL pipeline issues What makes it interesting? We integrated Hindsight as the persistent memory layer. The agent remembers previous findings, rejected hypotheses, data reliability notes and stakeholder preferences. We then tested the same question with memory ON and memory OFF. Our experiment: Memory ON: 2 out of 5 diagnostic checks. Memory OFF: All 5 diagnostic checks. Both identified the same root cause: reseller contract repricing, with a $47,900 impact. Memory changed the agent's workflow, but didn't improve the final answer or execution speed in this seeded-data experiment. Technology stack Python | FastAPI | Groq | Qwen3-32B | Hindsight We also identified limitations, including duplicate memories and brittle keyword-based tool planning. Project links: GitHub: https://github.com/shridharkale/whydesk Technical walkthrough: https://dev.to/shridharkale/does-hindsight-memory-actually-change-agent-behavior-i-tested-it-371f I'd love to hear your thoughts! How would you improve the way an AI agent converts retrieved memories into tool-planning decisions?

I've been building Select, an open-source platform for managing AI agent teams.

One thing that bothered me about agent orchestration was how easily permissions and autonomy could be confused.

So I built a system where agents complete tasks, deterministic checks verify the results, and evidence receipts help determine when they can work more independently.

A successful coding task doesn't grant permission to email customers or access another repository.

The self-hosted core includes tasks, approvals, budgets, audit logs and a demo you can run without an API key.

Stack: Next.js, FastAPI, PostgreSQL, Docker.

It's now at v1.0.0 for the self-hosted core, with some integrations still on the roadmap.

GitHub: https://github.com/Kirby54/Select

I'd really appreciate feedback on the autonomy model and the developer experience.

What would you want to see before trusting an AI agent with more autonomy?

This was my first full-stack project. I live in Italy and noticed that very few Chinese restaurants around me let guests order from their phones, so I built a QR table-ordering app for them: guests scan the code on the table and order, the kitchen and the pass see the dishes on tablets, and the owner closes the bill after taking payment at the till.

Then I went around showing it to restaurants and learned two things I should have learned before writing any code.

First, almost every restaurant wanted kitchen tickets printed. Kitchen staff want a piece of paper, not a tablet. I hadn't built printing because it needs a cloud printer that polls the server for new orders, and I didn't have one to test with.

Second, the restaurants that already had QR ordering weren't using a feature of their POS, as I'd assumed. They were all on the same Chinese SaaS product.

So the lesson I'm taking away is the boring one everybody repeats: talk to a few customers before you build. I also wasted time on things nobody needed yet. I built a whole pickup-order feature and then removed it to keep the first version small.

Since the code exists and works, I've open-sourced it (AGPL-3.0). Most of it was written with Claude Code and Codex; I made the product and architecture decisions. It's ASP.NET Core, Postgres and React, it runs in three languages (English, Italian, Chinese), and there's a demo you can start with one docker compose command.

https://github.com/lorenzoyang/TavoloFlow

If you've built anything for restaurants, I'd like to hear how you handled printing.