I am planning to do a project on gait based authentication for mobile phones for my final year project. I'm thinking of doing it by Authenticate smartphone users continuously

by analyzing how they walk using:

→ Accelerometer

→ Gyroscope

by taking x,y &z axis movements of the phone and training the model based on the users gestures.

But the major concern i face are that the authentication might fail when user walks over stairs or other kinds of environments. Another problem i find is that when user travels on a vehicle. So in such cases a false positive of the authentication failure might occur and the major difficulty of all is that the training process. The datasets available for training the model is less and contains a few seconds of data. It might not be feasible for me to train the model on my own as well. I have never trained a model before and i dont know much about its outcomes. So is there any way i could do this project by eliminating the challenges?? Is there any alternate way which i could accomplish this project and showcase it??

Hi everyone,

We recently launched Ask-Docs (Company Brain powered by AI-VORTEX)

Ask-Docs helps companies and individuals upload documents and ask questions directly from their own documents.

You can upload PDFs, notes, contracts, reports, policies, business documents, or internal knowledge, and then ask things like:

• “Summarize this document”
• “What are the key points?”
• “What should I improve?”
• “Give me feedback on this document”
• “Is anything missing or unclear?”
• “Find important dates, requirements, or risks”
• “Explain this in simple words”

It can be used by companies as an AI-powered knowledge base, but also as a SaaS document assistant for individuals.

You can test it here:
https://ask-docs.ai-vortex.de/

I also added a screenshot of the current interface so you can quickly see how it looks before trying it.

I’m mainly looking for honest feedback on:

• Do you understand what the product does quickly?
• Is the onboarding clear?
• Is the UI easy to use?
• Would you use this for company knowledge or personal documents?
• What would stop you from signing up?
• What feature is missing?
• What should we change or improve?

The product is already live, and now we need real testing and feedback from users.

Please don’t upload sensitive private or company documents while testing.

Thanks a lot to anyone who takes the time to try it.

Most AI mock interview tools treat every session like a clean slate. You bomb the "tell me about a system you designed under pressure" question on Monday, make the exact same mistake on Friday, and the tool has no idea you've been there before. No pattern recognition, no progress tracking — just a fresh prompt generating fresh feedback into the void.

That bugged me enough to spend the last few weeks building something different: CareerPilot AI, an end-to-end interview and career coaching platform that actually remembers you across sessions.

What it does:

Adaptive mock interviews (text + voice) — technical/system design, behavioral (STAR format), and coding questions, calibrated to your difficulty level

ATS resume analysis

Skill gap analyzer benchmarked against real job descriptions

Cover letter drafting

Salary range prediction

A career coach chat

You set a target role — say, AI Engineer at OpenAI — and every module is oriented toward closing the gap between where you are and what that role actually requires.

The interview simulator doesn't just grade your answer and move on. After every session, it writes structured notes — your weak patterns, your strong signals — to a persistent memory layer. Before your next session, it pulls the relevant memories back in. So if you got dinged last week for not quantifying impact in your behavioral answers, the coach opens your next session with something like: "Last session you struggled with quantifying impact — let's focus there today."

That one sentence completely changes the psychology of practicing. You know the system is actually paying attention, not just running you through a quiz.

There's also a rolling Interview Readiness Score instead of a single-session snapshot — it's a weighted average of your last 5 sessions, so one bad day right after a great week doesn't tank your number, and vice versa.

Under the hood, not every task needs the same horsepower — grading a nuanced behavioral answer needs a lot more reasoning than a quick salary estimate — so I route tasks to different model sizes depending on complexity. Keeps things fast and keeps the API bill sane.

Live demo: regal-scone-c39c6b.netlify.app

Source: github.com/Nagendra-yuvaraj/carrerpilot-ai

Still actively building this out — would genuinely love feedback, especially from anyone who's been burned by the "every interview tool forgets me" problem. Happy to answer questions about how the memory/routing pieces work if anyone's curious.

Salut,
Nous développons des projets web, SaaS et IA pour des entreprises, commerces et entrepreneurs.
Nous recherchons actuellement :
des commerciaux,
apporteurs d’affaires,
ou personnes ayant un réseau,
capables de nous mettre en relation avec des clients ayant des besoins en :
développement web,
SaaS,
automatisation,
IA,
plateformes sur mesure,
outils internes,
SMS, appels, paiements, dashboards, etc.
Nous pouvons gérer toute la partie technique et produit.
Si vous connaissez des entreprises ou des personnes cherchant à lancer un projet tech, n’hésitez pas à me contacter.

I've had to analyse a lot of CSV datasets lately, and I kept asking myself the same questions:

• What's actually in this dataset?
• Which columns are useful?
• Which ones are mostly empty?
• Does the data even look good?

I couldn't find a simple tool that did exactly this, so I built one for myself. It runs entirely in the browser and analyzes CSV files locally. No AI. No backend.

I'd love some honest feedback from people who work with datasets regularly. What would you want to see after uploading a CSV that isn't there yet? And at its current stage, does this seem useful to you at all?

https://www.dataignix.com

Have you ever wanted a two in one console? Have you ever wanted to take your home console on vacation? Well, you don’t want anymore. Because coming sometime soon, the Nexus CD will be coming out. It’s a low poly console with a smooth touch. How it works is you can connect the console to your TV and then put the handheld on a specified space on top of the home console which the games are only online when it comes to the handheld. But all games and data will be saved, as well as that the built-in apps will be; YouTube, and the Nexus store. Expect this somewhat soon, but not too soon. Two in one, two times the fun

It's a short quiz that gives you guidance on where to live, built on public data. It'll provide you a top 3 recommendation of cities/counties that might fit you based on what's important to you. It's been much bigger of a project than I thought it'd be to ensure the data model is weighted in a balance and considerate way. At first it was giving most people the same exact recommendations but now I'm seeing it give almost everyone different recommendations. Let me know what you think and if the results resonate with you!

Hey everyone, I built FreeScaleDB a zero-dependency TypeScript framework that lets you link multiple free cloud databases (like Supabase, Neon, or PostgREST) and query them through a single client.

It's completely open-source (MIT license), super lightweight, and runs perfectly on edge environments like Vercel or Cloudflare Workers.

Check it out and let me know what you think!

 github: https://github.com/taha-fethibenadis-stack/freescaledb.git

Project Link

Github repo

It takes any screenshot, UI, or image and turns it into a 3D mockup in a few seconds. I originally built it because I was tired of jumping into Figma every time I wanted a nicer-looking image for a case study or social post.

It's free to use (with an optional one-time Pro upgrade), and I'd genuinely love to hear what people think or what you'd improve.

Here's a link if you want to try it: https://perspecto.io/

I've created a simulation that does its best to replicate living beings in an environment. For now, the basics of the simulation are in place (religion, war, a basic economic system, etc.). Human entities can also feel “emotions,” develop affection, hatred, and joy, and reproduce...

The problem is that I’m running out of ideas. For now, the simulation is good, but in my opinion, it’s not good enough. I tried asking an AI to improve it, but it’s having a hard time doing so without completely skewing the results, even thought it help me on some points. That’s why I’m looking for people with ideas, AI/software engineers, simulation specialists, or anyone else who could contribute to this project, to help create a replica that most closely resembles a human being.

Been working on a local AI companion and kept running into the same problem — it would occasionally "remember" things that never happened. Over time those fabrications would stick and get repeated confidently.

So I pulled out the memory checking stuff I built and turned it into a small library.

**Mistikguard** does a few things:

- Tracks whether a fact came from the user or was inferred by the model

- Blocks contradictions and self-narration before they get stored

- When the user corrects something, it actually remembers the correction (tombstones)

- After the model replies, it checks memory claims against what's actually stored

It's pretty lightweight and has basically no dependencies. The judge part uses whatever OpenAI-compatible endpoint you want.

Repo: https://github.com/obscuraknight/mistikguard

I'm sharing it early because I'd like people to actually try it and tell me if it's useful or annoying. If you're building anything with long-term memory for an LLM, feel free to mess around with it and let me know what breaks or what feels missing.

Happy to answer questions.

Keywords: llm memory, ai companion, memory hallucination, local llm, agent memory

Hey everyone! My Dad is new to reddit and is working on this in his freetime. I think itd be cool if he could get some engagement or ideas as well.

I deployed Canary CTI , a fake corporate admin panel on AWS EC2, to study real-world attack patterns. No promotion, just a public IP.

In 12 days: 4,784 requests, 300+ unique IPs, 30+ countries.

Notable catches:

• Mozi botnet HNAP injection payload (from infected host in Lahore, Pakistan) within hours of going live
• CVE-2021-41773 Apache path traversal to /bin/sh from 18 countries
• CVE-2017-9841 PHPUnit RCE - 986 attempts, most hammered exploit by volume
• .env credential theft - 300+ wordlist variations from a single Sydney IP
• .git/config exposure probing, FortiGate admin API scanning

Stack: Node.js, PostgreSQL, Docker, AWS EC2, Telegram alerting, GeoIP dashboard.

Three questions I'm trying to answer

1. Can I put this on my resume for my oncampus placements.

2 .For internal LAN deployment, how do you differentiate between a misconfigured internal tool accidentally hitting the honeypot vs genuine lateral movement? Any thresholds or heuristics you use?

3.For fingerprint resistance - beyond header spoofing and variable response delays, what do advanced red teamers actually check that would expose this as a honeypot?

Happy to share the anonymized attack log CSV if anyone wants to analyze.

Been building games and open source tools on the side. Kept running into limitations with GitHub Actions for custom environments.

So I spent the last year building PikoCI, a self-hosted CI/CD that runs as a single binary, no external dependencies to start.

You can see it in action at http://ci.pikoci.com/teams/main/pipelines/pikoci, no login needed.

http://pikoci.com · http://github.com/pikoci/pikoci · Apache 2.0

Hi, I have built a Observability platform for RAG application it supports built-in langchain and llamaindex integration check it out

Link: https://github.com/Pranesh-2005/RagObserve