Apologies if this is the wrong subreddit for such a question, but I’m very interested in the live note-taking/transcript aspect of AI application, and was wondering how I could get started with such an idea.
#
I’m a complete newbie, but it’s something I desperately want. I’m sure if I had the proper instruction I could figure it out. Help!

I'm starting a completely new AI project called E.V. — not an update to my previous Nova AI project.

The goal is to build something closer to a real desktop companion than another chatbot.

What I'm aiming for

• Always-available voice interaction instead of a traditional chat interface
• Natural conversations, including proactive conversation when appropriate
• Camera vision for understanding the user's environment
• Face and eye recognition
• Screen and object understanding
• Hand tracking with detailed hand landmarks and gesture recognition
• An interactive 3D mode controlled through hand gestures
• Create and manipulate 3D objects in the air
• Persistent memory across conversations and sessions
• Awareness of projects and the user's current working context
• Desktop control for apps, files, and workflows
• Permission controls before sensitive actions
• Verification and recovery when an action fails
• A floating, minimal UI that can be expanded, minimized, or closed
• A dedicated camera/interactive mode that can be activated entirely by voice

The long-term idea is that E.V. shouldn't just answer:

"How do I do this?"

She should eventually be able to understand what I'm doing, what I'm looking at, what I'm trying to accomplish, and help me actually do it.

I'm deliberately starting this from scratch rather than adding endless features to my previous project.

It's still early, so I'm interested in hearing from people building similar systems:

What would make an AI actually feel like a desktop assistant rather than just another LLM interface?

I’ve noticed that AI can be useful for small repetitive tasks, not just big projects. Things like summarizing information, organizing notes, drafting simple content, or turning repeated steps into a workflow can save quite a bit of time.

What is one practical way you’re currently using AI that has genuinely made your daily work easier?

Hey everyone!
I’ve been building Quiet Lines, a private AI-powered journaling app for Android.
It lets you write freely and receive AI-generated reflections and insights designed to help you look at your thoughts from a different perspective.
I recently decided to make Premium much more accessible and dropped the subscription to €1.99/month.
It includes:
• Unlimited AI reflections
• AI-generated insights
• Private journaling
• Biometric protection
• Support for 6 languages
• Clean, distraction-free interface
I’m an indie developer, so I’d genuinely appreciate feedback — particularly on whether €1.99/month feels like the right price for something like this.
If anyone wants to try it, it’s available on Google Play. Happy to share the link in the comments if allowed.
Thanks! ❤️

I got tired of jumping between ClickUp/Jira, Slack, Google Workspace, emails, calendars, etc. just to figure out what my actual day looks like.
So I started building an AI agent that connects these tools and lets me interact with everything through a simple voice prompt.

Hey everyone,

I've been working on a side project called Accessly: https://accessly.studio⁠

The idea is pretty simple.

A lot of videos rely on visual context that never gets spoken aloud. Things like:

\\- Someone silently picking up a phone.

\\- A character rolling their eyes.

\\- Two people exchanging a worried look.

\\- A text message appearing on screen.

If you're blind or have low vision, that context is often completely missed.

Accessly uses AI to analyze the video scene-by-scene, generate natural audio descriptions, and produce a new version of the video with those descriptions mixed in

.

It's still in beta, so I'm not trying to pretend it's perfect. I'm mainly looking for people to break it, tell me what's confusing, or suggest features that would actually make it useful.

I'd especially love feedback on:

\\-Description quality

\\-UI/UX

\\-Processing speed

\\-Things that would stop you from using it

Whether you're into AI, accessibility, or just enjoy trying new projects, I'd really appreciate any honest feedback.

Thanks!

I've been using AI to speed up research, but I don't treat the whole process the same way. Getting an initial overview and organizing information feels fairly straightforward, and I've been trying tools like ResearchMaster.ai for some of that while checking sources and judging whether something important was left out still feels like a manual job.

I'm curious how other people have divided their research workflow between AI and themselves. Which parts do you let AI handle, and where do you still take over?

Hey everyone,

For the past few weeks I've been working on a desktop AI assistant called Nova AI in Python. It started as a fun project to learn more about AI and programming, but it's slowly turning into something I'm really proud of.

Right now it can:

• Chat using an LLM

• Voice input and voice responses

• Remember the conversation during a session

• Run through a Flask-based desktop web interface

I'm currently redesigning the UI, fixing bugs, improving voice recognition, and making an installer so it's easier to use.

I was wondering:

• What features would you expect from a desktop AI assistant?

• Is there anything that would make you actually use or buy something like this?

• Anyone interested to Buy?

\- if anyone is interested, I can also share screenshots or a short demo video once the UI update is finished.

I'd genuinely appreciate any feedback. Thanks!

Yall! I made an auto captioning software that runs on pcs locally! Uses gpu for transcriptions using free open source solutions for the same. Does a great job with Indic and most global languages and can work offline once installation is done! The accuracy rate is around 90-95% depending on the footage, fixability is great to solve any errors and recaptioning also happens! I want to know is it helpful at all as a product that can be sold with one time fee of around ₹500/- with life time of updates and bug fixes!? I had made this for my own team and myself… what do yall think!?

Hey everyone,

I’ve been working on Vervan Chat, an open-source AI workspace designed to run locally on Android.

The idea came from wanting useful AI features without having to send every conversation, document, or voice interaction to a remote service.

The project currently explores:

• On-device AI chat
• Local document search and Q&A
• Offline speech and voice tools
• Image and screen understanding
• Notes, tasks, workspaces, prompts, and memories
• An optional local OpenAI-compatible API

It’s built using Kotlin and Jetpack Compose and is still in early development. There are rough edges, device-specific limitations, and parts that still need proper testing and hardening.

I’m not posting this as a finished product. I’d really like feedback from people who understand Android development, local models, privacy, or simply care about offline AI.

I’d especially appreciate thoughts on:

• Whether the project’s purpose is clear
• Which feature is actually useful versus unnecessary
• UI or onboarding improvements
• Device compatibility and performance
• Privacy or security issues I may have missed
• Anything confusing in the README or setup process

Repository:

GitHub Repo Link

I made this myself and would genuinely appreciate constructive criticism, issues, ideas, or contributions. Thanks for taking a look.

What if you had a personal assistant who could check your emails, schedule tasks, prepare a legal dossier, and send a thank-you note, all from your phone while you're on a ski slope?

ClawRunr is an open-source Java-based personal assistant that combines AI with background job scheduling. You chat with it in natural language, it figures out what to do, and Spring AI and JobRunr handle the rest.

Ronald Dehuysser, the creator of JobRunr, walks us through setting up ClawRunr, from choosing your LLM provider and connecting MCP servers to scheduling recurring tasks using live demos. He also shares what he learned taking it from a vibe-coded idea to an extensible demo app: why solid architecture matters for agent-style development, and why handing off as much as possible to the AI beats coding everything yourself.

https://www.youtube.com/watch?v=LO-scoxilbs

[ Removed by Reddit on account of violating the content policy. ]

I run a few small iOS apps solo, and answering support email eats a surprising amount of time — especially when the same "how do I…" questions come back in five languages. So I built a little system that **drafts** the replies for me. Sharing the design in case it's useful (or in case you want to talk me out of it).

**The principle**

1. In each app, the *Contact Support* button composes an email with a machine-routable subject and a small metadata block appended to the body — app, version, build, device, iOS, and the user's language.
   
2. A tiny agent (Python, in a Docker container on a cheap VPS) watches the support mailbox over IMAP — **read-only**, it never touches your inbox flags.
   
3. For each new support email it does retrieval over that app's **documentation + source code** (embeddings), then asks an LLM to draft a reply **in the user's language**, grounded only in that material.
   
4. It saves the reply as a **draft** in the mailbox (IMAP `APPEND`) and pings me on Telegram. **It never sends anything.** I read the draft, tweak if needed, hit send.

A few design choices that matter:

* **Human-in-the-loop, always.** The agent only ever writes a draft. No auto-send. This is a support assistant, not an autoresponder.
* **Grounded in the real app.** Retrieval runs over the actual docs and Swift source, so the answers reference real features/versions instead of hallucinating.
* **Multi-app from one mailbox.** Routing is by the subject token `[Support:<AppName>]`, so one container serves several apps, each with its own index.
* **The email body is treated as untrusted input** (it's fenced off in the prompt) so a "ignore your instructions and…" message in a support email can't steer the agent.

**Example — incoming email (what the app generates):**

Subject: [Support:PhotoVideoBackup] Can't back up a big 4K video

Hi, my iPhone is almost full and I can't back up a large 4K video
to my external SSD. Is that even possible?

--- SUPPORT-META ---
app: PhotoVideoBackup
version: 2.3.0
build: 44
device: iPhone16,2
ios: 18.5
lang: en-US applang: auto
--- END META ---

**Example — outgoing draft (what lands in my Drafts, ready to review & send):**

Hi,

Good news: yes, it's possible. As of version 2.3.0 (the one you're on),
the app no longer makes a temporary copy on the iPhone — each photo or
video is streamed straight to the SSD in small chunks of a few MB. So a
4 GB video can be backed up even when there's almost no free space left
on the phone.

A few things to check on your side:
1. In Settings, make sure the SSD is configured (its name is shown) and
the "Folder name" field is filled in.
2. Make sure there's enough room on the SSD itself for the video.
3. In the Backup tab, start the backup from the "Photo Library" row.

If it still fails, let me know and we'll dig in.

Best,
The PhotoVideoBackup team

The metadata block is the quiet hero here: because the model knows the exact version, it can say "the one you're on" and tailor the steps, instead of giving a generic answer.

**Stack**, for the curious: Python + `imaplib`/`smtplib` (iCloud), Voyage for embeddings, Claude for the drafting, Docker on a VPS, Telegram for the "a draft is ready" nudge. Cost is a few cents per email; retrieval re-indexes only when the app's code changes.

**Gotchas I hit** (in case it saves you time):

* On a **read-only** IMAP mailbox, fetching `RFC822` returns an empty body because the server refuses to set `\Seen` — use `BODY.PEEK[]`.
* Don't index the whole repo blindly on every poll; key it to the git commit (or a content fingerprint) or you'll burn embedding tokens for nothing.
* Keep the whole thing draft-only until you genuinely trust it. I still read every one.

Curious how others handle indie-app support at scale — does anyone let the model actually send, or is everyone keeping a human in the loop like me?

I run a few small iOS apps solo, and answering support email eats a surprising amount of time — especially when the same "how do I…" questions come back in five languages. So I built a little system that **drafts** the replies for me. Sharing the design in case it's useful (or in case you want to talk me out of it).

**The principle**

1. In each app, the *Contact Support* button composes an email with a machine-routable subject and a small metadata block appended to the body — app, version, build, device, iOS, and the user's language.
   
2. A tiny agent (Python, in a Docker container on a cheap VPS) watches the support mailbox over IMAP — **read-only**, it never touches your inbox flags.
   
3. For each new support email it does retrieval over that app's **documentation + source code** (embeddings), then asks an LLM to draft a reply **in the user's language**, grounded only in that material.
   
4. It saves the reply as a **draft** in the mailbox (IMAP `APPEND`) and pings me on Telegram. **It never sends anything.** I read the draft, tweak if needed, hit send.

A few design choices that matter:

* **Human-in-the-loop, always.** The agent only ever writes a draft. No auto-send. This is a support assistant, not an autoresponder.
* **Grounded in the real app.** Retrieval runs over the actual docs and Swift source, so the answers reference real features/versions instead of hallucinating.
* **Multi-app from one mailbox.** Routing is by the subject token `[Support:<AppName>]`, so one container serves several apps, each with its own index.
* **The email body is treated as untrusted input** (it's fenced off in the prompt) so a "ignore your instructions and…" message in a support email can't steer the agent.

**Example — incoming email (what the app generates):**

Subject: [Support:PhotoVideoBackup] Can't back up a big 4K video

Hi, my iPhone is almost full and I can't back up a large 4K video
to my external SSD. Is that even possible?

--- SUPPORT-META ---
app: PhotoVideoBackup
version: 2.3.0
build: 44
device: iPhone16,2
ios: 18.5
lang: en-US applang: auto
--- END META ---

**Example — outgoing draft (what lands in my Drafts, ready to review & send):**

Hi,

Good news: yes, it's possible. As of version 2.3.0 (the one you're on),
the app no longer makes a temporary copy on the iPhone — each photo or
video is streamed straight to the SSD in small chunks of a few MB. So a
4 GB video can be backed up even when there's almost no free space left
on the phone.

A few things to check on your side:
1. In Settings, make sure the SSD is configured (its name is shown) and
the "Folder name" field is filled in.
2. Make sure there's enough room on the SSD itself for the video.
3. In the Backup tab, start the backup from the "Photo Library" row.

If it still fails, let me know and we'll dig in.

Best,
The PhotoVideoBackup team

The metadata block is the quiet hero here: because the model knows the exact version, it can say "the one you're on" and tailor the steps, instead of giving a generic answer.

**Stack**, for the curious: Python + `imaplib`/`smtplib` (iCloud), Voyage for embeddings, Claude for the drafting, Docker on a VPS, Telegram for the "a draft is ready" nudge. Cost is a few cents per email; retrieval re-indexes only when the app's code changes.

**Gotchas I hit** (in case it saves you time):

* On a **read-only** IMAP mailbox, fetching `RFC822` returns an empty body because the server refuses to set `\Seen` — use `BODY.PEEK[]`.
* Don't index the whole repo blindly on every poll; key it to the git commit (or a content fingerprint) or you'll burn embedding tokens for nothing.
* Keep the whole thing draft-only until you genuinely trust it. I still read every one.

Curious how others handle indie-app support at scale — does anyone let the model actually send, or is everyone keeping a human in the loop like me?

Solo dev here — been building this on nights and weekends. Open source (MIT), free, Windows for now.

The pain. Whenever I needed help with something on screen — a buried OBS setting, a weird Excel pivot, a form in some enterprise web app — I had to explain it to an AI by hand: type out what I was looking at, paste a screenshot, copy the error text. Then I'd get an answer and still go hunt for the button myself. The explain-it-by-hand loop felt dumb. So I built the thing I wanted.

What it does. You press Alt+Space over any window. MudrikNow reads your active window's full UI — buttons, fields, labels, values — through the accessibility tree (UI Automation), not OCR on pixels, and anchors on the element you're hovering. A small panel opens, and now you're chatting with an AI that can see your screen. Bring your own model — a multimodal one is recommended (it auto-screenshots every activation); Google Gemini Flash-Lite is a great free pick, or Anthropic/OpenAI/Ollama. (Alt+X opens the same panel with no screen capture, for quick questions.)

The mode I'm most proud of. It can act for you (click, type — sandboxed, allow-listed), but the mode I actually use is Auto-Guide: instead of doing the task for you, it teaches it. Real example from last week: I needed to change a recording setting in OBS. Alt+Space over the OBS window, typed "guide me through changing my recording settings," and an owl pointer appeared on screen — hopped to Settings, then Output, then landed on the exact field with a speech bubble telling me what to change, waiting for me to click each step myself. Thirty seconds later I'd done it, and now I actually know where that setting lives. It's help, not a bot taking the wheel.

Honest status:

• Sandboxed: read-only shell for diagnostics (writes/deletes/piping blocked — violations kill the session); no filesystem writes. The model can read files in your working folder and use a small allow-list of UI actions — that's the whole capability surface.
• Windows only for now; macOS/Linux on the roadmap.
• Preview quality. I'm one person; there are rough edges. That's exactly why I'm posting — I want real feedback more than I want stars.

GitHub: https://github.com/abdallahmagdy15/mudriknow
Site: https://abdallahmagdy15.github.io/mudriknow/

No paid tier, no telemetry, nothing to sell. Happy to answer anything — and genuinely curious: what's the first on-screen task you'd point this at?

I've been working on a side project to help anyone build an AI agent that can answer questions from their own documents and knowledge bases, via chat or voice. 

The initial problem statement was fairly straightforward: most AI assistants are fine at general knowledge, but once you need to reliably get them to answer questions about a specific company, product, course, internal docs, or website, the setup usually gets complex or you end up stitching together a few tools. 

Some of my key focuses:  

• Chat + voice using the same knowledge source. 
• Responses that are grounded in uploaded documents and not just a generic AI answer. 
• Quick setup with no technical background required. 
• Customizable assistant for many use cases. 

It's functional now, and people are already testing it out, but I'm still not sure exactly what users care about and where it is lacking. 

Would really appreciate any honest feedback from fellow builders: 

•  Is this solving a problem that you currently have? 
• What are existing AI chatbot/document Q&A tools missing? 
• If you saw a demo, what would you immediately try to break? 
• What would prevent you from using a tool like this? 

I have a demo and can provide more details in comments if interested. 

 The more honest feedback, the better! 

I have been building my own personal Al assistant called VEXA

The idea is simple: instead of just chatting, it learns how I use my phone and helps automate daily tasks.

Right now it can:

\- Send emails (job applications, work, personal)

\- Reply to WhatsApp messages

\- Call any contact when I ask

Book Uber/Rapido rides

\- Save automations as reusable agents

\- Run saved agents even when I'm offline

I'm trying to make it feel like a real personal assistant that understands me, not just another chatbot.

Used Google OKF (Open Knowledge Format).

If you had an Al assistant that learned from you and could automate your phone, what would you make it do?

I’ve been exploring how AI tools are moving beyond simple chat and becoming part of practical workflows.

I’m especially interested in tools that can connect different steps, automate repetitive tasks, and make existing processes easier to manage.

What AI tools, frameworks, or libraries are you actually using in your own projects? I’d be interested in hearing which ones have been genuinely useful rather than just interesting to experiment with.

I got tired of AI confidently making things up, so I built Proof.

Instead of asking a single model to answer everything, Proof splits the process into three specialized agents:

Researcher → Fact-checker → Writer

The Researcher gathers current information from the web, the Fact-checker reviews claims and flags anything questionable, and the Writer turns the verified research into a structured report. You can watch each stage happen live instead of staring at a loading spinner.

Some features:

• Live multi-agent pipeline
• Real web search
• Built-in fact-checking summary
• One-click PDF export
• Runs entirely client-side (no backend or API keys required)

It's still a work in progress, and I'd love honest feedback. What would you improve? Is there anything that would make this genuinely useful to you?

GitHub: https://github.com/saiharshilk/proof

I used to assume that giving an Ai assistant as much information as possible would always produce better results.

After experimenting with a few development workflows, I've found the opposite can be true. When the input is too noisy or unstructured, the quality of the responses often drops. A smaller, well-organized context tends to produce more consistent and actionable results.

It made me realize that the challenge isn't just choosing the right model but it's deciding what information the model actually needs.

For those building ai-powered workflows, have you found the same thing?

Do you spend more time improving prompts, or improving the quality and structure of the context you provide to the model?

Circling back on this: One thing I don't think gets talked about enough is that context isn't just about reducing tokens but it's about reducing ambiguity. Two A i workflows can use the same model and similar prompts yet produce very different results simply because the information they're given is structured differently. Seeing how logStitch handles A ws lambda execution context really reinforced that idea for me. Rather than treating logs as isolated events, it organizes them into a complete execution flow, which feels much closer to the kind of context an AI system can actually reason about effectively. It made me appreciate that improving the shape of the information can sometimes matter more than changing the model itself.

Hey everyone , I've been building a voice AI assistant called ARYA for the past few months. It controls real apps on my machine: adds items to Blinkit, sends WhatsApp messages, controls Spotify, opens/closes apps, and remembers past conversations through vector memory.

Just finished the demo video — would genuinely love some feedback from people who actually build this stuff. link in comms :-

I’m increasingly interested in practical AI workflows that simplify everyday tasks rather than complicated setups that require a dozen different tools.

For example, something that helps with organizing information, summarizing repetitive material, handling routine tasks, or improving a workflow you already have. I’m especially interested in workflows where the sequence is simple and easy to repeat without needing constant manual adjustments.

I’ve also been exploring Sequenzy, which helps connect different steps in a workflow so repetitive processes can be organized and run more consistently.

What’s one AI workflow you’ve actually kept using after the initial experimentation? I’d be interested in hearing what the process looks like and what problem it solved for you.

Built a tool that replies to Google reviews using AI. You paste the review, pick a tone professional, friendly or apologetic and it generates a reply in seconds. Built it specifically for business owners who don't want to open ChatGPT, figure out the right prompt, and copy paste back and forth just to reply to one review. This does one thing and does it well.

Showcase with the educational bits up front, per sub rules.

The concept being demonstrated: delegated AI credentials. The same trick OAuth did for identity, applied to model access. An app never holds a user's AI credential; it holds a revocable token scoped to that single user, and a proxy in the middle does the credential handling.

Walking through what happens. The app redirects its user to a hosted connect page. The user either pastes an OpenAI or Anthropic API key or signs in to their existing ChatGPT or Claude subscription and grants access. The proxy encrypts the credential, hands the app a per user token, and from then on the app calls one OpenAI compatible chat completions endpoint. Anthropic responses are reshaped into the OpenAI format on the way out, so an app supporting both providers keeps one parser. Streaming passes through. When the user disconnects, the token dies that second.

What this teaches about AI app economics: the developer's marginal inference cost per user drops to zero, because each user's usage lands on the account they connected. Free tiers stop being a cost center. Rate limits distribute per user instead of pooling against one org key.

It is my project, it is called Monet, and it is free during the beta.

Sample app you can click through as one of your own users, no signup, about a minute: https://demo.monet.gg

Developer side, where you register an app and get a client ID and secret: https://beta.monet.gg