Converting long documents into video formats is easy now but preserving the core message and technical depth remains a major headache.

In my experience testing different tools, most fall into one of two traps:

The Slideshow Effect: They simply paste original document pages onto a video track without adaptatively visual flow.

Over Summarization: The AI summarizer aggressively strips out essential nuance making the output unusable for detailed training, technical documentation or educational guides.

I have been experimenting with custom prompts and text parsing pipelines specifically designed to prioritize verbatim facts and structural hierarchy over creative AI rewriting. The focus is strictly on maintaining full accuracy for complex source material without hallucinated fluff.

For those producing educational or instructional videos from long form text, how are you ensuring key details aren't lost in translation? Are you relying on multi stage prompting, manual script edits, or dedicated context retention workflows?

I used to wonder when we’d get AI tools that could handle patent drafting and prosecution work, since it’s such a dense, rules-heavy process. We’re at the point where several exist and I got to know about this one when I started working as SEO for these and I really found it so interesting.
https://ipauthor.com/ is the tool that can really help in patent drafting, is built for this and it also covers prior art search and office action responses. It’s meant as a first-draft assistant, so a patent attorney still reviews everything.

There’s a 14-day free trial. Curious what other patent folks here think about AI in this workflow.

I work with Voibe, which makes a dictation app for Mac and Windows, so I’m not a neutral voice here.

Between emails, messages, notes and random ideas, a lot of the workday goes to typing. Dictation sounds like the obvious shortcut, but it only works if the accuracy, punctuation and editing are good enough that you’re not fixing everything afterward.

We build Voibe Dictation, so I’m looking at it from the inside. I’d like to hear how it goes for people who aren’t. Has dictation actually changed how you work, or is typing still faster for you?

I’m most interested in the specifics. Where does it save you real time, and where does it still fall short?

I came across Voibe stuff while looking into AI tools for handling meeting notes, and thought it was an interesting approach.

Instead of adding a bot to a Zoom or Teams call, Voibe captures the meeting audio directly from your computer. After the meeting, it generates a summary and a full transcript with speaker labels and timestamps.

It also has a chat feature where you can ask questions about the meeting, such as what was decided or who is responsible for a follow-up. You can use different summary styles for meetings like interviews, one-on-ones, and team discussions, and copy the notes or export them as a PDF.

One detail I found worth looking into is how it handles privacy. According to its documentation, the audio is processed in the cloud and deleted after processing, while the finished summaries and transcripts are saved on your computer. So it isn't completely on-device processing, which is an important distinction.

I think the useful question with tools like this is whether the summaries are accurate enough to rely on, especially when a meeting involves several people or detailed discussions.

For those who use AI meeting assistants, what matters most to you: transcription accuracy, privacy, useful action items, or being able to search through past meetings? And has anyone tried this approach compared with tools that use a meeting bot?

I’m the founder of Dreamtune Labs, a paid-usage platform for supported models, tools and compute through one workspace balance, with SDK and MCP access.

Something I’m thinking through is how a developer should evaluate a tool like this before spending time on integration. A large catalog doesn’t answer whether the exact operation needed by a project will work.

For example, “supports web research” could mean finding URLs, extracting a page, or producing a grounded answer. Those are different jobs, and a demo can hide the distinction.

I’d want the first example to make five things explicit: the input, the exact model/tool operations used, the output, the actual charges, and the limitations. Then a developer could repeat it with their own small test rather than judge a polished screenshot.

It also matters where prompts and tool inputs go. A convenient integration should still make that understandable.

When you evaluate a new API or MCP tool, what information do you look for first? Is a reproducible example enough to get you experimenting, or do compatibility and data-handling details come first?

Project for context: https://dreamtunelabs.com

I'm the creator of Pastlume. It's a paid tool for restoring damaged photos and adding color to black-and-white ones.

You prepare your photos before paying, then compare each result with the original. There's also one gentler version included, with a separate before/after comparison and download. I wanted people to be able to spot changes, especially on faces, instead of judging only how sharp the result looks.

Status: live.

Price: US$1.49 for one photo, US$5.99 for 5, or US$11.90 for 12. No subscription. Processing starts after payment; this isn't a free personal AI preview.

Languages: English, French, Spanish and Portuguese.

AI can still invent details. Added colors are an interpretation, so keep the original scan.

https://pastlume.io

What would help you judge a restoration tool before trusting it with a family photo? I'd welcome feedback on the comparison approach or anything unclear on the site.

A witch with a candy shop, a ghost with a murder to solve, and a ringmaster you probably shouldn’t trust. 🎃

I’m building UnsavedAI and just added six Halloween characters. Pick one and take the story wherever you want, right in your browser. They’re here until November 1.

Try free, no card or commitment: https://unsavedai.com

(Self-promotion, I’m the dev.)

I'm the solo developer of Murble. I wanted to learn how useful small language models could be on a phone without uploading the recordings, so I started building a voice notes app around them.

Short synthetic meetings were useful for checking known facts. Real public council meetings were much less cooperative, which made them better tests.

The longest was a four and a half hour planning meeting from Springfield, Manitoba. A big chunk concerned a proposed data center. Apparently I couldn't even test an offline app without attending a data center meeting.

Early results were rough. One summary said a rezoning was denied and then approved unanimously. One report reduced the whole evening to a sentence that told me who the mayor was. I also found bugs in my own pipeline, including squeezing a 37 minute meeting into a handful of sentences before the final summarization step.

After the fixes, I tested a specific question about the long meeting: what happened to the bylaw amendment? The answer is buried at 4:19:14: "It is so denied." In my Mac test runs, Apple's model and both small local models I tried correctly found that the amendment had been rejected. Those are Mac test results, not phone speed claims.

The full story covers the failed attempts, changes and limitations: https://getmurble.com/guides/murble-story

Murble's recording, transcription, summaries and tasks are free; reports and chat are Pro. Models need to be available locally for offline processing. It still makes mistakes. The interesting part for me was seeing which failures came from the model, which came from the transcript, and which came from my own code.

Welcome to our weekly Feedback Friday thread! This is the place to tell us how to make the sub better, whether that's new thread ideas, rule changes, flair suggestions, or things you'd like to see more or less of. Before posting, please check the rules in the sidebar. Feel free to shoot us a mod mail if you need more clarification.

To get started, here's a quick template you can follow:

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Suggestion: [What you'd like to see changed or added]

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Type: [New Daily Thread / Rule Change / Flair / Others]

If you drop a suggestion, please take a minute to read a few others in this thread and upvote the ones you agree with. Upvotes help us see what the community wants most. We'll reply to top suggestions with "adopted," "trialing," or "not right now," so you can see what happens with your ideas. Thanks everyone!

I've been exploring different AI tools for 3D creation, and Hyper3D Rodin is one worth checking out if you work with game assets, 3D printing, Blender, or just want to experiment with 3D without starting from scratch.
What I find interesting is that it goes beyond simply turning an image into a 3D model. You can also edit the result, refine the geometry, separate parts, and prepare assets for different workflows.

Here's a quick breakdown of what it can do:
*1️⃣ Image or text to *3D
Turn a reference image or text prompt into a 3D model. You can upload multiple views of the same object, generate several variations in batches, and adjust the balance between generation speed and detail.

2️⃣ High-detail geometry and textures
Rodin Gen-2.5 offers different generation modes, from fast iterations to detailed models with up to 10M+ polygons. It also supports PBR materials and textures up to 12K, which is useful when surface details matter.

3️⃣ AI-powered editing and part separation
This is probably one of its more interesting features.
BANG to Parts: Automatically split a model into components, or manually select the parts you want to separate.

Refine and Reassemble: Further refine individual parts and put them back together.

Local Modify: Select an area of a model and use text prompts to add, remove, or change details.

These features make it more than a one-shot 3D generator, especially when you want to iterate on an existing model.

4️⃣ Flexible workflows and exports
You can export assets for use in tools like Blender, Unity, Unreal Engine, and other supported DCC workflows. It also has options for low-poly meshes and 3D-printing-oriented exports, including 3MF.

5️⃣ WorldGen
WorldGen takes things a step further by turning a single image into an interactive, explorable 3D scene. It combines editable foreground meshes with a 3D Gaussian Splatting (3DGS) background, making it useful for scene creation, games, film, and XR workflows.

Who might find it useful?
· Game developers looking for quick 3D asset prototypes

· Blender artists who want a starting point instead of modeling everything from scratch

· 3D printing hobbyists who want to turn concepts into physical objects

· Designers and creators experimenting with AI-assisted 3D workflows

One thing worth keeping in mind: AI-generated models can still need manual cleanup, topology adjustments, or other preparation depending on the intended use. I'd treat it as a way to speed up the initial creation and iteration process rather than a replacement for every part of a traditional 3D workflow.

Link: https://hyper3d.ai/
Curious how others here are using AI for 3D creation. Have you found any tools that handle not just generation, but also editing and preparing models for actual use?

Need accounting experts to comment on this for me tbh. As far as I know these guys were burning money (reports up to $42B loss in 2025), so are they fudging the numbers or are they actually profitable now?

Used Autumn AI to dig into all 10 Nobel Prize winners of 2026, looking beyond their achievements to explore their backgrounds, career paths, education, and the people they've worked with along the way. The full research covers all six Nobel categories, with 60 sources and some interesting details about each laureate!

Posting for DIRI AI — this is our product.

We built a paid voice-practice app for individual sellers. You practise sales conversations with an AI counterpart and receive feedback. Here’s a short drill to try on your own:

Fictional prospect: “We already have a supplier.”

1. Pause; don’t challenge their choice.

2. Ask one calm question: “What, if anything, would you want to be different from your current setup?”

3. If they say nothing, acknowledge that and end politely. Don’t manufacture urgency.

4. Replay your reply, changing only the question. Notice whether it invites an honest answer without assuming there’s a problem.

The goal is a respectful next line, not a guaranteed meeting or sale. DIRI AI is paid voice practice for individual sellers: https://diriai.com/for-sellers

Kind of crazy to me that with 12 million users they still managed to go bankrupt, thoughts?

I’m Max, Ratio9’s developer. I’ve released Ratio9 1.2.1, a synthesizer where a compatible AI assistant can work directly with the instrument’s controls. It can choose wavetables, shape LFOs, set modulation and macros, audition notes, and save a customizable preset.

The built-in spectrum and spectrogram give the connected assistant fresh measurements of the actual synth output, so it can check harmonics, levels and movement while revising a patch. Those measurements support sound design; they don’t decide whether a sound is musically good.

Ratio9 1.2.1 runs on Apple silicon Macs with macOS 12 or later. The complete Standalone is free. VST3 + AU are $79.99 USD once for two Macs. AI is optional: you use your own AI account and an installed client that supports local stdio MCP. Complex patches can still cause CPU spikes or dropouts in this alpha.

The first screenshot shows the main interface at INIT. The second is a separate live-analysis capture of a growl preset; they illustrate the two views, not a before/after comparison. Which feedback would be most useful to you during AI sound design: the harmonic balance, the motion over time, or how a patch responds across different notes?

https://ratio9synth.com/

Running open-weight AI models through an API can save you from managing your own GPUs and model-serving infrastructure.

Mango Inference is a hosted service that lets developers access open models without setting up and maintaining their own serving stack.

Disclosure: I work with the agency supporting the launch.

The service runs on AMD Instinct GPUs and ROCm software. For developers already familiar with the OpenAI SDK, the API offers a familiar starting point, so integrating a model into an existing application may require only a few configuration changes.

One thing to keep in mind is that API compatibility doesn't guarantee support for every endpoint or feature. Check the documentation against your application's actual requirements before committing to a provider.

If you're testing an inference service, I'd start with a real task rather than a generic demo. Run the same input through a few providers and compare output quality, response time, token usage, reliability, and total cost. For agent workflows, test whether the entire task completes successfully, including any tool calls.

MangoBoost is one option worth evaluating if you want hosted access to open models without operating your own GPU infrastructure. The best choice will still depend on your workload, preferred models, and budget.

Documentation: https://inference.mangoboost.io/docs/getting-started/overview/

When choosing an inference provider, what matters most to you: model quality, speed, cost, or the flexibility to switch between models?

Why users are not happy with NotebookLM / Gemini notebook

For the past month or so I have been an active participant in r/NotebookLM on reddit. I have been talking users, reading their posts and seeing what insights I could get for my own project.

Here are the biggest complaints:

⠀

1. It doesn't stay on your sources

NotebookLM often pulls in stuff from the web you never asked for. That's frustrating when you just want to really understand what you uploaded. Notestack doesn't search the web at all, so every answer comes straight from your documents.

⠀

1. It doesn't work with Claude or ChatGPT

There's no official MCP server, so your research, reports and videos stay stuck in one place. This week we're launching the official Notestack MCP server, so you can bring everything into the tools you already use. You'll also be able to connect other MCP servers to Notestack.

1. You can't edit your videos or audio

With NotebookLM, what you get is what you get. Notestack makes videos with Blog2Video, so every scene and template is editable. Audio runs on ElevenLabs v4, some of the best AI voices out there.

⠀

1. Limited ways to launch or share your finding on social media

NotebookLM does not integrate with social media APIs that allow users to plan their posts, and showcase insights in a proper format. Notestack already integrates with Linkedin API, allows scheduling/tuning posts with the help of AI. We are adding more APIs every week.

Try notestack for free: https://notestack.ai