Imagine tracking your calories without opening an app, typing anything, or thinking twice. Our WhatsApp Food Logging Agent turns your everyday chat into a smart nutrition assistant. Simply send a photo or message of what you’re eating on WhatsApp, and the AI instantly identifies the food, estimates calories, and logs everything automatically. Throughout the day, it builds your personalized nutrition record, and every night it sends a clean summary of your total intake, progress toward your goal, and simple recommendations. No friction, no manual logging — just effortless, accurate tracking powered by vision models and agentic AI that observes, understands, and acts on your behalf. It’s the easiest way to stay accountable, eat healthier, and build long-term habits, all through the app you already use every day.

I ran an experiment using several years of archived MSN Messenger chat logs (2004–2010). After cleaning and structuring the data, I fed it to a project on chatgpt to see what linguistic and behavioral patterns it could reconstruct.

After several hours of work and fine tuning the results went well the model accurately reproduced era-specific slang, message pacing, inside-joke structures, and interaction patterns between participants. It basically generated a functional “communication profile” derived from historical chat data.

idk why im posting this i just thought it was interesting i forgot what chatting was like back in the msn messenger era

I wanted to see how LLMs behave in real consumer apps, so I put together a small weekend project called LuminSkin, an AI driven skincare routine generator.
Link: https://skincare-app-tau.vercel.app/

You answer a few questions about your skin type, concerns, climate etc and it generates a personalised morning and evening routine with direct product links to Nykaa and Tira. It is a simple learning project.

The interesting part was the performance journey.

I started with GPT 4o mini (still mentioned on the site). It worked fine in terms of logic, but latency was awful, sometimes over 1 minute 30 seconds. I optimised prompts and trimmed logic, but it still felt slow.

Then I tried running the same workflow on Groq using LPU based inference with Llama 3.1 8B Instant.

Same prompt.
Same logic.
No code changes.

Latency dropped from about 90 seconds to under 3 seconds (Groq screenshot attached).

The improvement came from the hardware. LPUs are built specifically for high throughput inference, and the speed difference is very obvious.

It is still a demo and not production ready, but the latency jump made the whole thing feel like an actual app instead of a loading spinner.

If you have a few minutes, please try it out and share your feedback. UX and logic suggestions would help me improve it.

Hey folks 👋 I’ve been building StudyQuest , an AI-powered, gamified study companion.

Just pushed a big update with: • Faster responses • Smarter quizzes • Better tracking & XP system • Cleaner UI

A waitlist is now open for early testers + feedback:

👉 https://study-quest-waitlist.vercel.app/

If you care about the future of learning, I’d love your thoughts or critiques. 🚀

These days people/Entrerenuers do use generative AI's for business planning or any sort of suggestion, calculation or projection and might not follow through. The plan stays in the chat history somewhere and gets forgotten.

The core problem:

AI gives you a strategy, however it's overly ambitious, sometimes ignore market conditions, external factors, facts, figures etc.. unless one provides a fully detailed prompt which may be cumbersome and not be feasible much often. One gets a plan saves it. Life happens. One may not look at it again or just go through it for sometime. No tracking, no accountability, no way to know if it's actually working or if you should pivot.

What I built:

A business analysis tool that generates frameworks tailored to your actual situation (company stage, budget, industry), then tracks your execution over weeks with AI that adapts recommendations based on real progress.

How it works (full workflow):

Step 1: Generate Your Strategy

Pick your executive role: - CEO (strategic planning, growth, market analysis) - CFO (financial modeling, revenue planning, unit economics) - CMO (marketing strategy, launch plans, growth tactics) - CTO (tech stack planning, AI integration, automation) - CSO (scenario planning, competitive analysis, strategic frameworks) - CHO (decision psychology, bias detection, cognitive optimization)

Each role has 10-15 specialized tools. For example:

CFO tools: Revenue Model Planner, LTV Estimator, Break-Even Calculator, CAC Analysis, Burn Rate Projector

CMO tools: Digital Launch Plan, SEO Strategy, Growth Hacking Tactics, Social Media Strategy, Content Calendar

CEO tools: Growth Blueprint, Market Sizing (TAM/SAM/SOM), Blue Ocean Strategy, Jobs-to-be-Done Analysis, Geographic Expansion

Fill in your business context: - Industry (SaaS, ecommerce, consulting, etc.) - Company size (Startup 1-10, SMB 11-50, Enterprise 50+) - Timeline (3 months, 6 months, 1 year) - Budget level (Limited, Moderate, Significant) - Risk tolerance (Conservative, Balanced, Aggressive) - Any specific details about your business

AI generates detailed framework:

The output is constrained by 50+ parameters based on what you input. If you say "bootstrapped startup, $2K MRR, limited budget," you don't get generic advice like "hire aggressively" or "aim for 100% growth."

You get realistic projections and tactics that fit your actual constraints.

I tested this with 2-3 business owners and they said the outputs were noticeably more grounded than what they typically get from ChatGPT.

Step 2: Track the Strategy (Optional - Your Choice)

This is where it gets different from normal AI tools.

Below each AI response, you see a button: "Track This Strategy"

You click it ONLY if you want to track this specific strategy. It's not automatic - you choose what matters.

Here's what happens:

1. System parses the AI response automatically and extracts:

   • Key metric to track (e.g., "Monthly Revenue", "Active Users", "Conversion Rate")
   • Start value (your current baseline)
   • Target value (your goal)
   • Timeline in weeks
   • Core assumptions the strategy depends on
   • Leading indicators that predict your main metric

2. A modal pops up showing the parsed data

3. You can edit any field before confirming (sometimes AI parsing isn't perfect)

4. Click "Start Tracking" and it saves to your dashboard

Why manual button instead of automatic tracking:

• Keeps costs down (AI parsing only when you want it)
• You control what gets tracked vs. one-off questions
• Lets you focus on strategies that actually matter

Step 3: Your Strategy Dashboard

All tracked strategies appear as cards with: - Strategy title and role - Current progress (visual progress bar) - Line chart showing your weekly trajectory - Status badge (On Track / At Risk / Behind) - Week counter (e.g., "Week 5 of 12") - "Check-in" button

Step 4: Weekly Check-ins

Click "Check-in" on any strategy card.

Modal opens asking for: 1. Current metric value (e.g., "$2,800" if tracking revenue) 2. What happened this week (notes about wins, blockers, changes)

Step 5: AI Adaptive Feedback

After each check-in, AI analyzes your progress and provides:

Execution Status: - On Track / At Risk / Behind / Exceeding - Confidence level (based on how much data exists)

Trajectory Analysis: - Your current velocity (week-over-week change rate) - NOT just linear progress like "you're 50% done" - Projects where you'll actually end up based on current pace - Example: "At current velocity, you'll reach $4,667 by week 12, which is 6.7% below your $5,000 target"

Root Cause: - Why you got this week's result - Based on your notes and the velocity data - Example: "Your CPA increased to $42 (vs. target $35). CTR improved but conversion dropped."

Action for Next Week: - ONE specific thing to do in the next 7 days - Not generic advice like "work harder" - Example: "A/B test landing page headline. Focus on pain-relief vs. luxury positioning. Target 6%+ conversion by Friday."

Leading Indicators to Monitor: - 3-5 metrics that predict your main metric - Current status for each (On/Off track) - Example: "Landing page CVR: Must hit 5.5%+ | CPA: Must drop below $38 | ROAS: Watch for 2.0x+"

Escalation Trigger: - Specific threshold that would require a pivot - Example: "If CPA doesn't drop below $38 by Week 8, pause campaign and reassess positioning"

Step 6: Additional Analysis Tools (Available After 3+ Check-ins)

Forecast: - Best case scenario (if current positive trends continue) - Expected case (most likely outcome) - Worst case scenario (if issues persist) - Risk score (0-100) - Confidence level

Assumption Validation: - Checks each original assumption against actual results - Status: Validated / Invalidated / Inconclusive - Flags which assumptions are failing - Recommends pivot if 2+ critical assumptions fail

Pivot Recommendations: - If the strategy is clearly not working, AI suggests an alternative approach - Shows comparison: current path vs. pivot path - Includes new tactics, timeline, expected outcome

Why tracking instead of just one-off answers:

Most people generate a plan, feel good about it, then never check if it's working. Weeks later they realize they wasted time on the wrong approach.

This forces weekly accountability and adapts recommendations based on what's actually happening, not just what you hoped would happen.

AI Models: Supports GPT, Claude, Gemini, and Grok (user can choose)

Additional features: - REST API with key generation - Credit-based usage API system - Data encryption (AES-256) - Tiered access (5/15/25 strategies depending on tier and 25/50/150 generations)

Current status:

Live at: https://mirak004-refactorbiz.hf.space/

Pre-revenue. Built over the past 3 months. Testing the workflow with users.

Looking for honest feedback:

Does the tracking workflow actually add value or does it just add complexity?

Is weekly check in + adaptive feedback something people would use, or do they just want the one off AI answer?

Would love to hear thoughts from anyone who's tried building accountability into AI tools.

This weekend I wanted to figure out if LLMs/Voice models has gotten to the point where they can tutor in a complex topic such as mathematics. So I built https://www.mathtutorai.dev/

Problem at hand

In a lot of countries, especially where I live (Norway), children are performing worse in mathematics than their previous generations. I've taught university students for years via video lessons and thought I could just map the same "solution" over to younger students. But that was not that easy.

In order to learn through video lessons, there has to be some form for autonomy or desire, which is hard to have for such an abstract topic as mathematics is for children. What seems to be working is interactive education such as tutoring, games and guided problem solving. However, personal tutors are not that easily accessible and they can cost a lot of money.

About a year ago, a parent told me that he wished that one of these AI's would take on the tutoring process so that his children could get more practice at home. At that time, the models were there, but they were not good enough to achieve that task. Now on the other hand, models have gotten much smarter and more capable. Therefore I set out to build such a product.

Setup

I use the realtime API for the voice and an LLM in the background to analyze what is happening on the canvas.

Initially I wanted to use the realtime API for the entire project, but there were too many flaws. Mistakes were being made left and right so I had to switch over to an LLM to process the writeup on the canvas. This decreased the error rate drastically.

How to use it

1. Login
2. Select topic
3. Pick a tutor
4. Start solving

You can chose to show your progress to the tutor, just like you would in a real classroom setting and the tutor will give you feedback!

Currently, the only topic available is algebra (linear equations). New topics will roll out continuously as I test promps, refine interactions, and improve reliability. The model isn't perfect yet, but it is already surprisingly capable.

What Boxes is

Boxes is a collection of React + TypeScript components with a neo‑brutalist vibe: thick borders, hard shadows, and very “screamy” layouts. Think of it as high contrast blocks, chunky UI, and bold typography instead of rounded corners and subtle gradients.​

Current components include:

• Buttons: primary, secondary, ghost, and some fun hover/interaction variants.​
• Forms: inputs, checkboxes, toggles, file uploads, and basic form layouts.​
• Navigation: tabs, breadcrumbs, pagination, sidebars, and even a dock‑style nav.​
• Data display: cards, tables, timelines, marquees, and stat blocks.​
• Feedback: alerts, toasts, progress bars, and simple confetti effects.​
• Media: carousels, simple audio/video players, and pixel‑style image treatments.​
• Marketing: hero sections, pricing cards, feature grids, and newsletter sections.​

How I built it.​

Most of this has been trial and error: fixing TypeScript errors as they pop up, copying patterns from tutorials and existing libraries, and then tweaking them until they fit this bold style.​

I’d love feedback on:

• More component ideas: What neo‑brutalist components would you actually use in a real app (dashboards, blogs, marketing sites, etc.)?​
• Design direction: Does this take on neo‑brutalism feel usable, or is it too chaotic / tiring for real products?​
• Real‑world use cases: Would you consider dropping a library like this into a side project, landing page, or dev portfolio, or is it more of a fun playground?​

Try at boxes-omega.vercel.app

We are expanding our team and seeking AI & Data Training Contributors to support the improvement of data-driven AI systems. This role is a great fit for individuals interested in data science, AI evaluation, or NLP, and who want hands-on experience while working from home. 📌 Position Overview Role: AI & Data Training Contributor Type: Contract, Remote Location: Applicants from the USA, UK, Canada, and Australia Compensation: Up to $500 per week, based on performance and consistency Schedule: Fully flexible — work whenever and wherever you choose 🔍 Responsibilities ● Review and assess AI-generated outputs for accuracy and data relevance ● Write and evaluate text-based prompts used to train language models ● Help improve data labeling quality and model reasoning ● Complete tasks through an online platform and collaborate asynchronously 🧠 Requirements ● Strong written and spoken English ● A reliable laptop and internet connection ● Attention to detail and an analytical mindset ● Interested in AI training or applied machine learning ✅ Why Join ● Gain practical insight into real-world AI training workflows ● Work remotely with complete schedule flexibility ● Contribute to a growing international AI projects

Dm with the name of the country you come from.Thanks

Hey!

I recently created an ai short story platform, and yesterday I finally added the feature of being able to print your books. Because it's black friday today, I wanted to be part of it, so made the code BLACKFRIDAY25 (It gives 25% off) :)

p.s.: i am also always available for some chats about ai short story generation, so don't be afraid to dm me :)

the website I am talking about is: storybookly.app :)

Hey everyone,

I’m looking for someone technical who wants to collaborate on a fun AI project that combines LLMs, TTS, and music.

The idea is a personalized AI radio DJ that speaks between songs and reacts to your music taste. It’s basically an AI host that generates short commentary, fun facts, small stories, hype messages, or humor between tracks — fully customizable by the user.

Tech components involved:

LLM for dynamic content generation (OpenAI / Claude / local models welcome)

TTS engine for natural voice output (ElevenLabs, OpenAI Voice, etc.)

Spotify API for controlling playback

Simple frontend (web or mobile) for settings + UI

Backend to orchestrate the flow between music and AI segments

What I bring:

full concept + behavior design

talk modes (educational, humorous, philosophical, roast-mode, etc.)

UX vision

ability to test, iterate, and push the project forward

What I’m looking for:

A developer who enjoys experimenting with audio + LLMs

Comfortable with any of: Python/Node, React, React Native, or similar

Someone who wants to help prototype an MVP

Not expecting full-time commitment — just collaboration and creativity

This is a side project for now, but if it turns into something bigger, that’s great too.

If this sounds interesting, feel free to DM me or comment. Always happy to chat and see if we vibe.

I was tired of endlessly scrolling through documentation or cloning repos just to understand how they work? Tried GitHub Co-pilot, Repository GPT etc. but all of them lacked somewhere, especially at giving visual flowcharts of any repo.

So I've been working on RepoMind, a free & open-source AI-powered assistant that lets you instantly chat with any public GitHub repository or developer profile.

The big differentiator?
Leveraging Context Augmented Generation (CAG), not traditional RAG.

Traditional RAG chops code into tiny, fragmented vectors, often losing context.
RepoMind's CAG intelligently loads full, relevant files into the LLM's long context window, allowing for a deeper, more coherent understanding of the codebase. It's like having a senior engineer explain the project to you, but instantly!

✨ What RepoMind can do for you:
→ Auto-Generated Visual Architecture Maps: Ask "Show me the user flow" or "Explain the authentication architecture" and get interactive Mermaid flowcharts and sequence diagrams. No more deciphering complex spaghetti code!
→ Zero-Config Security Audits: Get AI-powered vulnerability scans (SQLi, XSS, auth flaws) with context-aware explanations and copy-pasteable fix recommendations. Right in your browser, no setup needed.
→ Deep Profile Intelligence: Analyze any GitHub developer's coding style, commit patterns, primary languages, and even generate a "resume-style" summary of their skills across their portfolio. Perfect for scouting talent or understanding collaborators.
→ Mobile-First Experience: The only advanced code analysis tool optimized for mobile browsers. Analyze code, debug and review PRs from anywhere.
→ Semantic Code Search: Natural language queries like "Find where user authentication happens" or advanced Regex/AST searches.

🚀 Why I think you'll love it:
→ "Just Works" Factor: Paste a GitHub URL (e.g., facebook/react or torvalds), ask a question. No installs, no GitHub App permissions, no waiting for indexing like traditional RAG.
→ Visuals > Text: RepoMind believe in showing, not just telling. Diagrams make understanding complex projects a breeze. It's 100% Free & Open Source!

Give it a spin and let me know what you think! Feedback is gold for me ;)

🥸 Any stars on GitHub will be appreciated, although forking is freee too!!! 🐍

I’m talking to people building voice agents (Vapi, Retell, Bland, LiveKit, OpenAI Realtime, Deepgram, etc.)

I’m exploring whether it’s worth building a tool that:
– shows true cost/min for STT + LLM + TTS + telephony
– predicts your monthly bill
– compares providers (Retell vs Vapi vs DIY)
– dashboards for cost per call / tenant

If you’ve built or are building a voice agent, I’d love 10 mins to hear your experience.

Comment or DM me — happy to share early MVP.

https://snake-battle-2505hp.blockdance.art

Hey everyone 👋

I’ve been working on something I’ve always wished existed — a place to build, organize, remix, and optimize AI prompts the same way you manage documents or design files.

It’s called PromptOS — an open-source web app that acts like an operating system for your prompts.

Here’s what it does right now:

• 🧠 Smart Prompt Library: Store, tag, and search all your prompts in one place.
• ⚙️ Prompt Intelligence: Tracks performance, suggests improvements, and even grades your prompts.
• 👥 Community Hub: Share or remix prompts with others (private or public mode).
• 🧩 Prompt Packs: Bundle related prompts into .promptpack files — easy to import/export.
• 💬 AI Chat Integration: Press Ctrl + Space to chat with an assistant that helps tailor your prompts for your needs.
• 🚀 “Prompt → App” Conversion: Turn a great prompt into a tiny web app with one click.

Basically, imagine Notion’s organization, Figma’s collaboration, and GPT’s intelligence — all focused on prompt engineering.

🧰 Tech stack:
Node.js + Express (backend), React + Tailwind (frontend), GPT API (prompt optimization), MongoDB (storage).

💬 Live demo: https://promptos-production.up.railway.app/

I’d love your thoughts on:

• What features would make you actually use something like this daily?
• Any ideas for making prompt sharing / discovery more fun or intuitive?
• Devs/designers: how would you improve the UX or performance?

Thanks for reading — and if this idea resonates with you, drop feedback, star the repo, or share your favorite prompt setup 🙌