r/aicuriosity

I’ve been working on Murmur, a local text-to-speech app for Apple Silicon Macs.

The new feature I’m building is called Projects / Story Studio, and it solves a problem I kept running into:

TTS tools are fine for one-off clips, but messy for actual audio projects.

If you’re making a podcast segment, audiobook chapter, course lesson, ad, or game dialogue, you usually need multiple speakers, multiple takes, pauses, reactions, music, edits, exports, and a way to come back to the project later.

So I built a project-based workflow:

Write a script → assign voices → generate dialogue → edit clips on a timeline → add music/SFX → export final audio.

It supports things like:

• multiple scripts inside one project
• Host / Guest / Narrator / Character speakers
• inline tags like [pause], [laugh], [chuckle]
• per-block regeneration
• timeline editing with waveforms
• media lane for music and SFX
• ripple editing and gap tools
• WAV/M4A export
• transcript and stem export

Everything runs locally on Mac, so long scripts and voice samples do not need to be uploaded to a cloud service.

I’m still polishing the workflow and would love feedback from Mac users, especially people who make podcasts, audiobooks, courses, YouTube narration, or game dialogue.

Hi all, posting here after a long time.

I recently joined a new design & engineering studio as the technical co-founder, and I convinced everyone to open-source something we have been testing and using internally for our upcoming projects.

While designing and developing our projects we noticed how exhaustive the agentically-produced UIs have become. The same slop. Flat, purple or gray.

We wanted to give our products a distinctive character. A personality our upcoming users can resonate with. A design language that speaks to your eyes, while providing a pinch of nostalgia.

So we curated an internal design system which communicates directly to our agents, blurring the lines of tech-stack, setups, and dependencies. And now, we were just an MCP call or a single prompt away from writing depth-model, metallic finish components in whichever technology we were using. Everything from Tauri apps to native android.

Over time, we have polished it further, and now I am happy to announce that I convinced everyone to open-source "pudge-ui", our design system that teaches agents to make tactile, physical, 2000s-electronics interfaces. The best part is that it is tech-stack agnostic. You can be developing with any framework, for any platform, it will work.

pudge-ui: ui.pudgestudio.com

These are not just interfaces, your agent doesn't just digest how the component looks, they also digest how that physical component is supposed to function mechanically. So you are just a prompt away from adding motion, movement, haptics, and much more! The best part is the format.

It is not a component library and definitely not as versatile as something like shadcn. It has it's own use-cases, or maybe if you want to use your agent to create your own FL studio, without having it hallucinate on the complex interface.

It has 90+ (we are adding more) written specs. Each one describes the real hardware it imitates, how the mechanism works, the exact CSS, and the constraints. Agents read specs better than they read token files, so the output is faithful and works in any stack (CSS, RN, SwiftUI, Compose, Flutter). Add the MCP server and just ask your agent to "build a music player with pudge-ui." It is working magic for our team, and I would feedbacks from other people so we can improve it further.

Give it a star on github if you like it: https://github.com/pudge-studio/pudge-ui

Google is giving developers free access to Gemini 3.5 Flash through its API. You only need a Google account to grab an API key from AI Studio. No credit card or paid plan required.

The free tier includes Gemini 3.5 Flash along with the lighter Gemini 3.1 Flash Lite model. It comes with a 1 million token context window and native support for text, image, audio and video inputs. The endpoints are OpenAI compatible so existing tools and clients like Cursor can switch over without much hassle.

You get roughly 1500 requests per day on the free allowance and the limits reset daily per project. This setup suits personal projects, quick prototypes and side builds where you want strong performance without paying anything upfront.

A few practical points. Pro models are no longer available on free access. Free tier prompts can be used to improve Google models so it is better to skip anything private or sensitive. Some people testing it have found the per minute rate limits tighter than expected so it helps to check your actual quotas inside AI Studio before running big workloads.

To get started just visit aistudio.google.com, sign in and create your key.

For years, edge AI has promised intelligence everywhere. In practice, most "edge AI" still means sending data to the cloud, relying on large Linux systems, or requiring expensive accelerator hardware.

SuperESP changes that.

Built on Atome LM v2, SuperESP transforms a standard ESP32 into a tiny AI appliance capable of running twelve practical applications entirely offline.

No GPUs.

No subscriptions.

No datacenter.

Just a microcontroller that costs less than a cup of coffee.

Every claim is verifiable and tied to a script.

What SuperESP Actually Is

SuperESP is not another chatbot squeezed onto a microcontroller.

It is a collection of specialized ternary AI models designed to classify events, patterns, behaviors, and anomalies directly on the device.

The current release includes:

Agriculture monitoring

Voice commands

Motion recognition

Gesture detection

Sound event classification

Machine anomaly detection

Air quality analysis

Energy monitoring

Occupancy estimation

Wearable activity tracking

Water leak detection

Predictive maintenance

It comes also with :

+ ESP32 OS

+ Universal Installer

Check out everything :

https://github.com/TilelliLab/atome-lm

More info: github.com/Pengbinghui/pipeline-math

I got 10 million views in a month posting AI microdramas on Instagram Reels. The episode attached is one of them.

Here's what I did:

Most of AI video content on IG reels are one-offs. It pops, gets views, disappears, and you're back to zero. A show is different. People show up within 30 minutes of every post asking where the next episode is. They argue about the characters. One character I wrote as the villain got so popular that people begged me for weeks to bring her back, so I did, in another show, and she's still the most requested character on the account.

Every episode has three jobs.

Hook. The first five seconds stops the scroll. Nothing else. Get this wrong and nobody sees the rest.

Body. The plot moves fast. Every scene raises the stakes or twists them. The job is to make the next episode feel like a mandatory watch.

Cliffhanger. End on a question they need answered or an emotion they can't shake. This is what makes them follow you and come back tomorrow.

Then post every day. You watch three things: skip rate, retention (my best video run past 50 percent all the way through), and share rate. Then write the next episode directly towards whatever the audience reacted to. Read the comments and they tell you what they want.

The biggest unlock for me has been using an agentic studio for show creation. Consistency is one piece of it. Same characters, same locations, same props across all my episodes, because the second any of it drifts, the illusion breaks and people leave. But it goes way further than that. The agent helps structure the episode, tighten the dialogue, lock the styling. Designing the show and building the shots with an agent next to you instead of fighting the tools alone is a lifesaver.

Happy to answer any questions in the comments and let me know what you think about my episode!

ModelScope just introduced Agents A1, a 35 billion parameter mixture of experts model made for agent work that stretches over many steps. It targets search, engineering, scientific research, instruction following and tool calling in one package.

The model runs with a 256K context length and agent style reasoning that helps it plan and adjust across longer jobs. It shows leading results on benchmarks for extended search and research tasks along with strong instruction following scores.

It stays competitive with other models in the 35B range. Function calling support lets it connect directly to APIs, code interpreters, search engines and other external tools during its work.

xAI announced the Voice Agent Builder today. It is a no code tool for creating voice agents powered by Grok Voice.

The platform provides built in telephony, knowledge retrieval from uploaded documents, tool integrations for actions like calendar scheduling or API calls, guardrails to control what the agent can do, and observability to review calls.

Pricing runs at 0.05 dollars per minute of audio. New accounts get a free phone number to start with. You can also bring your own numbers via SIP.

It works with a direct speech to speech setup connected to the Grok model. This differs from the common approach of linking separate speech to text, language model, and text to speech services from different providers.

To build an agent you describe the call flow in plain language, upload documents for knowledge, set tools, and define any guardrails. The system supports over 25 languages and handles real world call conditions such as noise and accents. It launched in beta.

Google added two new models to the Gemini API and AI Studio today. Nano Banana 2 Lite focuses on image generation and runs in under four seconds per image while costing just 0.034 dollars for every thousand images. That speed and price point makes it practical for apps or workflows that need lots of quick outputs without running up big bills.

Gemini Omni Flash targets video editing work. It delivers top level results in that area at 0.10 dollars per second, the same rate as Veo 3.1 Fast. Both models are live now so developers and creators can start using them right away through the usual Google tools or Vertex AI.

The updates put more emphasis on real world speed and affordability for generative media tasks rather than chasing the biggest flagship model. People can test them directly in AI Studio to see how they fit into their own projects.

Some of our customers noticed Inter-1 (our omni-modal social-signal model) would occasionally "hear" a quote that didn't exist. Feed it a video with zero audio and ask what was said, and it would sometimes report: "Yeah, Friday at five." Verbatim. Same line, every time.

We assumed it had to be baked into the training data somewhere, so we went looking everywhere:

• 30,960 training records with datetime mentions → zero hits on the phrase
• 4,603 video transcripts → zero hits
• ~800 inference probes, 584 storage objects → zero hits

Turns out the phrase was sitting in our own system prompt — a worked example we'd written to show the model the expected output format, buried in a version our GEPA prompt-optimizer had shipped.

But that only explained where the words came from, not why the model would say them over total silence. So we ran two ablations in our internal eval harness:

1. Swap the word, keep the model: changed the prompt's example to "Tuesday at noon." Fabrication rate went up (37%→50%), and the invented quote tracked the swap exactly — Friday→Tuesday.
2. Swap the model, keep the prompt: ran the same byte-identical prompt through larger variants and an earlier checkpoint of our own model. They barely fabricated (0–2%). Only the further-post-trained Inter-1 confabulated at ~12%.

So it's not one bug, it's two stacked priors: the prompt supplied the script, but post-training is what gave the model the compulsion to recite something rather than report silence. Deleting the prompt example stops that one sentence — it doesn't stop the model from inventing different dialogue instead.

We think this is a textual/in-context variant of the audio-visual "Clever Hans effect" that's been documented for vision priors (model writes "thud" over a silent skateboard wipeout) — except ours shows the same reflex gets worded by whatever's nearest in the context window, which a vision-only diagnostic wouldn't catch.

Full writeup with the fabrication-rate forest plot and log data: https://www.interhuman.ai/blog/goblin-yeah-friday-at-five