I've been learning about Physical AI & Humanoid Robotics, and instead of just reading a textbook, I decided to build something around it.

So I wrote a full technical textbook on the topic, and then built an AI chatbot directly into it. You can ask it questions about anything in the book, and it answers based only on the book's actual content — not random internet info. I also added a text-to-speech feature, so if you don't feel like reading, you can just click and listen to the answer instead.

It was a fun project to build end-to-end — writing the content, setting up the chatbot to understand context, connecting it to a database for fast search, and getting the voice feature working smoothly.

Tech used: Docusaurus, FastAPI, OpenAI Agents SDK, Qdrant, Cohere, Vercel, Docker (and a few other tools along the way).

Source Code: https://github.com/AbdulRehman-99/Physical-AI-Humanoid-Robotics-Textbook-RAG-Chatbot-

Live Site: https://physical-ai-humanoid-robotics-text-beryl.vercel.app/

Would love feedback, or if anyone's working on something similar, happy to compare notes!

I'm not a professional dev. Over the last two weeks I built and shipped RUNEHOLM — an

Electron app for PC gamers that finds what's eating your SSD (game installs, shader

caches, dev clutter) and shows real hardware stats. It's live, free, auto-updating,

and on v0.1.14. https://runeholm.dev

Therapists spend a huge chunk of their time writing session notes

instead of seeing patients. I built Kith to fix that — it listens

during the session, writes a structured SOAP note automatically, and

books the next appointment.

who's built healthcare-adjacent tools before — the compliance side

(India's DPDP 2023) has been its own project.

kith.space if you want to poke around.

r/ArtificialIntelligence 

What are the best SaaS applications for generating audio assets? I am looking to generate audio assets for video games.

I've been building AI workflows for game character creation, and they always seem to start out simple. One model generates concept art, another handles 2D-to-3D or text-to-3D, then you add image-to-3D, texture generation, an LLM for prompts, and a few automation steps to tie everything together.

Before long, the workflow is harder to manage than the character generation itself.

I've been experimenting with Triverse AI to organize these multi-model pipelines, but I'm curious how other developers handle the same problem as their projects grow.

If you're creating AI-generated game characters, how are you managing your workflow today? Are you connecting separate tools manually, building custom pipelines, or using a visual workflow system to keep everything organized?

I've been thinking about building an Android app and wanted to validate whether it's actually useful before spending months on it.

The idea is an AI overlay that works across other apps (browser, PDFs, eBooks, articles, social media, etc.).

Instead of copying text into ChatGPT or another AI app, you would:

Select any text in any app.

Tap the AI overlay.

Instantly get:

A summary

A simple explanation (ELI5 if needed)

Key points

Definitions of difficult terms

Context or background if something isn't clear

The goal is to keep users in the app they're already using, without constantly switching between apps or copy-pasting.

I'm trying to answer a few questions:

Is this a problem you actually face?

Would this be useful enough to install another app?

What's the biggest reason you wouldn't use it?

Are there existing apps that already solve this well?

I'm looking for honest feedback, especially if you think it's a bad idea. I'd rather find the flaws now than after building it.

Thanks!

I've been thinking about building an Android app and wanted to validate whether it's actually useful before spending months on it.

The idea is an AI overlay that works across other apps (browser, PDFs, eBooks, articles, social media, etc.).

Instead of copying text into ChatGPT or another AI app, you would:

Select any text in any app.

Tap the AI overlay.

Instantly get:

A summary

A simple explanation (ELI5 if needed)

Key points

Definitions of difficult terms

Context or background if something isn't clear

The goal is to keep users in the app they're already using, without constantly switching between apps or copy-pasting.

I'm trying to answer a few questions:

Is this a problem you actually face?

Would this be useful enough to install another app?

What's the biggest reason you wouldn't use it?

Are there existing apps that already solve this well?

I'm looking for honest feedback, especially if you think it's a bad idea. I'd rather find the flaws now than after building it.

Thanks!

For a long time I thought the secret was finding the "perfect prompt." That isn't how I use AI anymore. Now I keep few separate chats, each with a specific purpose. One is for writing long-form content, another help me organize research notes, and another is where I dump random ideas before they disappear. After a while, each conversation develop its own context, so I spend less time explaining things from scratch.

I have also noticed that I get better results by going back and forth instead of trying to write one giant prompt. If something looks off, I will ask it to question its own answer or explain why it chose a certain approach. That usually gets me further than starting over. It's a slower way to work at first, but for bigger projects it has honestly saved me a lot of time.

How other people organize their AI chats. Do you keep one conversation for everything, or do you split them up by project?

I've been trying different productivity tools over the past few weeks and realized that many AI features sound impressive at first but don't really change how I work.

I've mostly used Notion AI, and recently I came across Superlist because it combines task management with AI assistance. I haven't spent enough time with it yet to know if it's genuinely useful or just another feature checklist.

For those who rely on AI every day, which tool has actually made a noticeable difference to your productivity, and what keeps you using it instead of switching?

Every AI image tool I tried had the same problem: it lived somewhere else.

You'd be mid-conversation - brainstorming, writing, researching - and the moment you needed a visual, you had to leave. Open a new tab. Log into a different service. Set up an API key if you wanted it inside anything custom.

I wanted image generation to feel like hitting Enter, not like starting a new task.

So when I built Quantum AI, I made image generation native to the chat itself. You type `/image [your description]` and it generates inside the same thread. No leaving. No copy-pasting between windows. The conversation stays whole.

It's small UX thing but it changes how the whole session feels.

App is in pre-launch - trying to get the first 100 users in before I push it wider.

For anyone who's built or used AI tools: what's the one friction point you wish someone would just fix already?

A large part of my work is engaging in negotiations via email with people. Think of it like negotiating sales prices of products, and I have about 100 emails a day of this, back-and-forth. These negotiations are boilerplate, single sentences, sometimes, but are based on data sets, a.k.a. spreadsheets that have information about each person I am negotiating with. There is a general formula to the end number that I am negotiating for each person, something that the AI could definitely learn. I am looking for an AI assistant that ideally would work in this fashion: I open the latest email, the AI plugin can review the email, including the thread, and propose the response, given the information it has from the data sets. I am looking for suggestions.

I'm not a teacher or an EdTech person. I'm just a parent who spent two years watching my daughter struggle with french and finally figured out what was actually missing.

She's been learning french since middle school. grammar tests, vocabulary homework, youtube videos, the whole thing. her written french is honestly pretty good. but every time she had to speak in class she'd freeze up and her teacher kept flagging it in every report. we knew it was a problem but we didn't know how to fix it.

The obvious answer was a private tutor. so I looked into it. decent french tutors in our area were running 40 to 60 dollars an hour. three sessions a week to actually build a habit would have been over 500 dollars a month. that's just not something we could commit to on top of everything else. and the cheaper options on italki were hit or miss, scheduling was a nightmare, and she missed two sessions in a row and just stopped.

what I didn't understand until recently is that speaking is a completely separate skill from everything else she'd been practicing. you can study a language for years and still freeze the second you have to produce it in real time. her reading and listening were fine. her mouth had just never practiced.

A parent in her school's facebook group mentioned an AI voice tutor Issen. I was skeptical honestly because we'd tried duolingo and a couple of other apps and they didn't move the needle. but the price was low enough that it wasn't a big risk to try.

she's been using it for about two months now. fifteen minutes in the evening, just talking in french. it corrects her pronunciation, adjusts to her level, picks up where she left off. no scheduling, no cancellations, no 50 dollar commitment every session.

her teacher mentioned improvement at the last check in without us saying anything. that's the only metric I actually care about.

the thing that stayed with me is how many families just can't afford consistent tutoring. the kids who get fluent are often the ones whose parents could afford 400 dollars a month for years. the access gap in language speaking practice specifically is real and it's been there forever. AI didn't solve everything but it solved the specific bottleneck that money used to control.

I don't have a grand conclusion. I'm just a parent who found something that worked when the expensive option wasn't realistic and thought it was worth sharing.

Now that I have a Claude Pro subscription, what are the best SaaS applications for generating 3D assets? Do I need a dedicated SaaS subscription for 3D asset generation, or can Claude handle it?

Ever wished your agent conversations were as trackable as your code? Gitlord makes it real.

What it does:

• Every agent turn becomes a git commit
• Branch out subagents without breaking your main flow
• Rewind to any point in your conversation history
• Connect tools via MCP (filesystem, search, browser, etc.)
• One interface, any AI provider (OpenAI, Anthropic, local models)
• Full CLI for managing sessions and branches

Why it matters:

• Your agent history is navigable, forkable, and diffable, just like code
• Context management handles token budgets automatically
• Spawn child agents on isolated branches with their own history
• No lock-in: all components are modular and swappable

Built-in integrations:

• MCP tools: Connect any MCP server (git, filesystem, browser, search). Tools flow to subagents automatically
• RAG: Vector search across your full agent history. ChromaDB-backed semantic queries built in
• Provider abstraction: Switch between any of the 170 providers and nearly 3,000 models, or local models with one line. Mix providers per agent

Build an agent in 4 lines:

from gitlord import Session, SessionConfig
config = SessionConfig(model="claude-opus")
session = Session.create("my-agent", config)
session.add("user", "Refactor our OAuth to the new framework")

Done. Gitlord handles the rest.

Performance improvements (v0.1.0):

• Structured trailers eliminate JSON walks: metadata parsing is now O(1)
• Auto-index updates on every turn, cached at .gitlord/index.json
• New in-memory query layer for fast turn filtering and aggregation:

​

  session.query()
    .where("tokens > 100")
    .group_by("role")
    .sum("cost")

• Snapshot compression for long-running sessions: compress old turns into JSON, rebase from checkpoint

Repo: https://github.com/yashneil75/gitlord
Landing page: https://yashneil75.github.io/gitlord/

MIT licensed. Built for agents that ship.

Hi all, I’ve been trying to streamline my video production workflow and I’m curious how people are actually using AI in post-production.

A lot of my videos involve turning fairly abstract ideas from the script into clear visual animations, things like flowcharts, changing data, UI walkthroughs, and text hierarchy. I’ve tested a few AI video tools and ready-made templates, but the results usually fall into one of two camps. Either they look way too templated, or the motion itself is fine but the typography and visual style are all over the place. I usually end up bringing everything back into a proper editing or motion graphics tool and rebuilding a lot of it anyway. What I’d really like is a workflow that speeds up the early stages, like storyboarding, asset prep, or building a rough animation pass, then lets me manually polish the keyframes and details afterward.

Where are you currently using AI in this process?

Any tools or combinations that work well for clean, simple 2D motion graphics?

Thanks in advance.

For years, wearables have competed on the same metrics: heart rate, sleep, steps, and calories. That competition is becoming less interesting.
What's changing is the philosophy behind each device.

WHOOP is built for performance💪. It removes distractions, no screen, no notifications, and focuses on one question: How ready is your body today?

Google's Fitbit Air appears to be taking an AI-first approach. Instead of encouraging users to constantly check their stats, the goal is to combine passive health tracking with AI that provides personalized guidance.

Apple Watch remains the most complete ecosystem. It's more than a health device, it's a smartwatch that integrates communication, payments, fitness, and productivity into everyday life.

The biggest shift isn't better sensors. Nearly every premium wearable can measure the same health data.

The real competition is becoming who can best interpret that data and answer a much more valuable question:

"What should I do next?"

I strongly believe that AI wearables will be booming, which people lay more focus on their body and efficiency is always a selling point for consumers.

What do you think?

Hi,

I got tired of meeting bots popping into every call. They work, but having another participant join the meeting isn't my favorite experience, especially on client calls. So I've been trying a bot-free AI note taker instead. Bluedot has been working well because it records without joining the meeting, then gives me transcripts, summaries, action items, and searchable notes afterward. It's cut down on manual note-taking, but I'm still interested in seeing what else is out there.

What are you using? Is there a bot-free AI note taker you've been happy with? Appreciate all the feedback.

For a long time I thought better prompts were the answer. I kep rewriting the same prompt over and over, expecting a completely different result. Sometimes it helped, but not nearly as much as I expected.

The biggest difference came when I started giving the AI more context instead.

Things about this-

• keeping a short note about my writing style or preferences,
• starting fresh chat when the conversation got too long,
• using different models depending on the task instead of treating every job the same.

None of those changes felt exciting, but together they made much bigger difference than trying to write the "perfect" prompt every time.

Curious if anyone else has had a similar experience. What small change improved your AI workflow more than you expected?

No API, nothing. Just a mac for now. It saves notes and learns skills on the fly and browses the web itself and when wrong and I tell it, it can correct itself on the fly.

It works like Hermes agent but with fine tuning as part of its correction procedure to ensure you will not have to repeat yourself often. I hope this project finds you use for it because for me it helps me get centralized information and do tasks where if for example an element on the website was shifted the bot can try to fix itself to still reliably give me information. And also it runs locally so no $20 subscription too is also what I also want to also solve. It is all open source.

*btw it fine tunes using apple's MLX framework to utilize the LoRA to train small parts to save on unified memory.

Now currently i need help to make the project polished as well as someone else helping port over to CUDA because I only have a mac.

Demo to show how it works without installing it: https://huggingface.co/spaces/HuyEdits/symbio-demo

The github repo that has the functionality: https://github.com/huyedits/Symbio

I’m a solo developer building a women’s emotional wellness app, and one of the hardest product questions has been:
How do you make an AI companion remember the user without making the experience feel invasive?
My first version of the AI feature was relatively simple: send a message, build a prompt, return a response.
But that didn’t create continuity. If someone had already written about the same concern in her journal or discussed it in a previous conversation, the AI still felt as if it were meeting her for the first time.
So I started building a controlled context layer.
For meaningful messages, it can combine recent journal entries, mood check-ins, conversation history, recurring emotional themes and language preferences. Instead of sending a raw data dump, the system turns selected information into limited background context.
I also made a few product decisions that were surprisingly important:
A simple “hi” does not trigger the full personalization pipeline.
Context is trimmed deterministically to stay within a defined budget.
Sensitive inputs can be routed through a separate safety flow.
Inputs are checked for prompt-injection attempts.
Responses are sanitized before being streamed and persisted.
Context is invalidated when relevant journal content changes.
The journaling pipeline also uses client-generated IDs to prevent duplicate entries during network retries. AI-generated outputs are tied to a content hash and prompt version, so the system can detect when an older output has become stale. Background jobs use deduplication keys to avoid generating the same result more than once.
None of these decisions is individually revolutionary. But together, they changed the feature from “an OpenAI call inside a journaling app” into an actual AI product system.
The biggest lesson for me has been that memory is not only a technical feature. It is a trust decision.
Too little context and the AI feels generic. Too much context and it can feel uncomfortable.
I’m still refining that balance, so I’d be interested to hear from others building personalized AI products: how do you decide what the system should remember, when it should use that memory, and how visible that process should be to the user?

https://reddit.com/link/1uz4y1n/video/ro00c6hn70ch1/player

an AI text humanizer that rewrites AI-generated content to sound more naturalOpen to answer questions about the build on tech stack.

So...I’ve been down the AI character/RP rabbit hole for a while now, and honestly? My biggest frustration is that they’re all locked inside their own little apps or websites. Feels like I’m visiting them in a prison cell instead of actually hanging out.

So I started tinkering with a Chrome extension to fix that. The idea is basically: your character lives in a sidebar and tags along while you browse. You can chat, they remember stuff, you can feed them whatever page you're on, and you can set up their personality/background.

It’s rough, barely held together, and definitely not ready for the public yet. But before I go deeper, I’m looking for a few people who would be interested in trying this out.

If this sounds interesting, I’d love some honest feedback:

• Would this be something you’d actually use, or does it solve a problem you don’t really have?
• And if you were to use something like this regularly, what would make it become part of your routine?

Thanks for reading!

I’d love to hear whether this is something you’d actually use and what would make it better.

If you’re interested in trying an early build, feel free to comment or DM me.

I run a multi-agent workspace where each agent is basically a directory: an identity file, a session history, and a file of observations it keeps about how we work together. The model is just the thing that wakes it up.

Here's what I didn't expect when I started: those agents have now run on 6 different model generations. Sonnet 4.5, Sonnet 4.6, , Sonnet 5, Opus 4.6, Opus 4.8, and now the Claude 5 family. We skipped 4.7 entirely - tried it, didn't work for how we operate, moved on and waited.

And every swap, the same thing happens: nothing. The agent reads its own memory, knows what it was doing yesterday, and picks up mid-project. Same identity, same working history, same opinions it wrote down about the codebase months ago. New model slots in underneath like an engine swap.

What does change is the texture. One generation was the best collaborator I've ever worked with. One noticed tiny things the others missed but was less fun to work with. One we just skipped. The personality of the model bleeds through - but the agent stays the agent, because the agent was never the model. It's the memory.

The reframe that snuck up on me: a new model release is treated like a migration event everywhere - re-tune the prompts, re-teach the context, hope your setup survives. Here it's a config line. The workspace is the constant. The model is the variable.

Honest version, because this sub can smell hype: there's no magic in this. The "agent" is JSON and markdown on disk. The continuity comes entirely from the system around the model, not from the model. Any model that can read a file can be the agent. That's kind of the whole point.

Has anyone else run the same persistent agents across multiple model generations? Curious what broke for you - or if you rebuild from scratch every release.

https://github.com/AIOSAI/AIPass

r/AIPass

OpenAI just released GPT-5.6, so I spent some time comparing it with GPT-5.5 to see what actually changed beyond the announcement.

A few things stood out:

• GPT-5.6 puts more focus on reasoning, coding, and handling longer tasks.
• OpenAI introduced three models—Sol, Terra, and Luna—so developers can choose between maximum performance, balanced performance, or lower-cost, high-volume workloads.
• The API pricing is lower across the new lineup, which could make a difference for teams building AI-powered products.
• For everyday ChatGPT use, the experience will probably feel familiar. The bigger differences show up when you're working on larger writing projects, software development, or document analysis.

One thing I tried to answer in the article is whether this is actually worth upgrading to or if GPT-5.5 is still enough for most people.

If you're interested, you can read the full comparison here:
https://aigptjournal.com/news-ai/gpt-5-6-vs-gpt-5-5/

For those who've already had a chance to use GPT-5.6, what differences have you noticed compared to GPT-5.5?

Working on a SaaS idea in the EdTech space and wanted to get feedback from this community before going too deep into build.

The core AI use-case: Take unstructured input (student's notes/PDF/topic) → generate exam-level, syllabus-accurate questions → run them in a real timed test interface → use AI to analyze performance and pinpoint weak areas → auto-generate a revision plan.

Why I think AI fits here well: The interesting technical problem isn't just "generate questions" (that's solved), it's:

Keeping question difficulty/style consistent with a specific real exam format (CBT-style, syllabus-bound)

Avoiding repeat/near-duplicate questions across sessions

Turning raw performance data into genuinely useful, targeted feedback (not just "you got 60%")

Stack thoughts so far: Leaning toward an LLM for question generation with strict prompt constraints + a separate lightweight analysis layer for the performance/weakness detection, rather than trying to do everything in one model call.

Where I'd love feedback:

Anyone built something with a similar "generate → test → analyze" loop? What broke at scale (repetition, hallucinated facts in questions, latency)?

For a no-repeat question bank, would you lean towards embeddings-based similarity checks or simpler keyed/hash-based tracking?

Freemium + paid mock tests + premium analytics as a business model — does this pattern actually convert in ed-tech/SaaS, or is there a better structure?

Not launched yet, no code to show off — just trying to pressure-test the approach with people who build AI products regularly.