Whisper

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Whisper AI

Claude Fable 5 built an entire Backrooms escape game from scratch

Play it here (free, no download, headphones recommended): https://backroom-escape.vercel.app/ Find 8 pages scattered inside the maze, reach the exit, don't get caught. Works on desktop and mobile. The maze is built fresh each run using a recursive backtracker with loops and open rooms punched into it. The textures, the mono-yellow wallpaper, carpet stains, ceiling tiles, even the normal maps, are all drawn onto canvases in code. The audio is 100% WebAudio synthesis. Fluorescent hum, footsteps, the heartbeat that kicks in when it's close, the whispers, the scream when it gets you. All oscillators and filtered noise, no samples. The monster is geometry primitives running A* pathfinding with a roam/stalk/chase state machine. It freezes when you look at it directly (Weeping Angel rules), hears your footsteps, and you can sneak past it. What caught me off guard was the bugs being genuinely weird browser-level stuff, not just logic errors. Sneak was mapped to Ctrl. Holding Ctrl + W closes your tab and you can't preventDefault that outside fullscreen. Moved sneak to C. The monster would sometimes hear you while sneaking. OS key-repeat fires keydown around 30 times a second, so holding the sneak key was toggling it on/off rapidly. Footsteps leaked through in the off-windows. Mouse would randomly stop working mid-game. Chromium silently rejects pointer lock requests for about 1.3 seconds after an unlock. Click back in too fast and the request just fails with no error. Fixed it with a lock queue and a watchdog. Camera would snap straight up the moment you loaded in. Chromium fires garbage mouse deltas right when pointer lock engages. Added a 200ms grace period. The thing that made it feel like an actual game and not just a tech demo was the audio. I was playtesting at 2am and at some point I just... didn't want to continue. That felt like a win. Source: https://github.com/StarKnightt/Backroom-Escape submitted by /u/Turbulent-Sink-6171 [link] [comments]

PullMD v3: I let Claude design the MarkItDown integration, and it argued for keeping three of our own converters instead

About six weeks ago I posted PullMD here: a self-hosted Docker stack that turns any URL into clean Markdown, with an MCP server so Claude Code / Desktop / claude.ai pull pre-cleaned content instead of burning context on HTML boilerplate. v3.0.0 is out, and it's a bigger jump than the version number suggests. Short version: PullMD is no longer just a URL reader. It now converts documents, images, audio and YouTube videos to Markdown as well, and the default output got leaner. And no, don't worry - I'd like to think I haven't enshittified the original thing. Everything that worked before still works, (almost) unchanged. More on that "almost" below. How it started A boring personal itch. I had a pile of HTML files saved on disk that I wanted to hand to Claude, and figured PullMD already does the extraction, so why can't I just drop them in. So I added local file conversion: drag-and-drop on desktop, file picker on mobile, same Readability + Trafilatura pipeline. Local files are never cached, no share link. A few days later Microsoft released MarkItDown, and the next step was obvious: if I can take HTML files, why stop there. PDF, Word, PowerPoint, Excel, EPUB. So we wired MarkItDown in as a sidecar. Then we ripped three of its converters back out MarkItDown is good at the boring part: parsing document formats. For three other paths, Claude made the case for keeping our own instead - and once the reasons were sitting there in the code, pulling them was an easy call. Audio. MarkItDown's default audio path hands the file off to a cloud speech service. For a self-hosted tool we wanted that to be the operator's choice, not a default - so audio runs against any OpenAI-compatible endpoint you configure: a local faster-whisper / Ollama, a Groq Whisper, OpenAI, whatever. Nothing leaves your box unless you point it there. YouTube. MarkItDown's converter calls the transcript API outside its try/except, so a blocked or transcript-less video throws and takes the whole conversion down - you even lose the title and description that were already in the page HTML. No proxy support either, and YouTube rate-limits datacenter IPs. So we kept our own keyless handler: title + description + transcript, configurable timecodes and chunking, language preference, a proxy option, and a graceful fallback that still returns metadata when the transcript is gone. Image captioning. Rather than route captioning through MarkItDown's own LLM client, we put the vision call in our own provider layer: any OpenAI-compatible vision endpoint - a local Ollama / LLaVA, OpenAI, Gemini via a compatible gateway (defaults to gpt-4o-mini). Zero coupling, so a MarkItDown update can't break it - and if you only want media and no document conversion, you don't have to run the MarkItDown container at all. The principle we wrote into the project notes: use MarkItDown for file formats; keep the fragile, third-party-dependent paths in our own hands. What's actually new in v3 Documents → Markdown - PDF, DOCX, PPTX, XLSX, EPUB, ZIP, CSV, JSON, XML. By URL, by upload (POST /api/file), or drag-and-drop in the PWA. Needs the MarkItDown sidecar; leave it out and web pages work exactly as before. YouTube transcripts - title + description + full transcript, no API key. Images & audio → Markdown - opt-in, local-model-friendly, off by default (no model calls until you set a key). High-quality PDF tables (OCR) - PDFs convert free through the sidecar by default; for table-grade output there's an opt-in OCR tier (?pdf=ocr, reference provider Mistral OCR at ~$0.002/page, your own key, falls back to the free path on failure). Opt-in so it never silently costs money - and no, I didn't bundle a 4 GB local OCR engine with a 60-second cold start; it's a pluggable endpoint if you want one. Clean body by default - the one breaking change (the "almost" from up top). The body is now just # Title + content; source URL, fetch date and metadata moved into the YAML frontmatter, so nothing's duplicated and agents read fewer tokens. One-line opt-out: PULLMD_SOURCE_HEADER=true. Frontmatter field allowlist - trim the YAML to just the fields your pipeline reads. Everything past plain web extraction is opt-in and degrades gracefully. Configure nothing and v3 behaves like v2 with a cleaner body. Upgrade / self-host mkdir pullmd && cd pullmd curl -O https://raw.githubusercontent.com/AeternaLabsHQ/pullmd/main/docker-compose.yml docker compose up -d # → http://localhost:3000 Self-hosters on v2.x: clean-body is the only breaking change, MIGRATION.md has the opt-out. :latest now tracks v3; pin aeternalabshq/pullmd:2 to stay on the v2 output format. How it got built Same as v1: Claude Code wrote essentially all of the code, mostly with Opus 4.8. What I actually contributed was the planning and the pushback. The workflow was the superpowers plugin end to end: brainstorming to pin the design before a line of code, writing-plans to turn that into a structured plan, then sub

Walkie-talkie mode for Claude: interrupt mid-response by speaking

For those who use Claude through OpenCode (terminal-based TUI), I made a plugin that lets you redirect Claude by just talking. When TTS is playing and you need to correct direction: Type /ptt — aborts the current response, starts mic Speak your correction ("no, use SQLite instead") Type /ptt again — transcribed and sent as your next message Or just speak over the TTS without typing — voice overlap detection handles it automatically. All runs locally via whisper.cpp (offline, private). Uses edge-tts for the voice output. Free for basic use, Pro ($29) for unlimited. Website: https://interrupt.camaramagic.com if you want to check it out. submitted by /u/pystar [link] [comments]

What will be the next breakthrough in ASR? [D]

Hey All, I am currently working on ASR models, and I have gathered some recent literature. From my literature search, it seems like the ASR models are getting more and more powerful due to two main things. Because pseudo-labelled data is growing, supervised models are rising rapidly. Whisper-large-v3 has been trained on 5M hours of weakly supervised data, and Nvidia Parakeet v3 has been trained on 660k hours of labelled data (open-sourced). Funny enough, Nvidia Parakeet v3 actually beats Whisper-large-v3 on almost every benchmark, even though it has a smaller model size and smaller data scale. So clearly, scale is not everything. New architectures are on the rise; We used to have self-supervised + CTC to solve the ASR task, but now it seems like Transducer, and Token-Duration-Transducers are taking off. As well as attention encoder-decoder architectures (Qwen) that are all trained in a supervised manner. Now, given that the labelled data is very huge, and the new architectures are coming up, are we saying bye to the self-supervised learning approaches like Data2Vec2.0, WavLM, etc., for ASR, and will we only use them for general-purpose speech tasks? This is actually not similar to how computer vision operates now. Dinov3 is a self-supervised approach that is extremely performant in segmentation, classification, depth estimation etc but I do not see this in the speech domain now. ASR is dominated by these huge supervised architectures (which is a dense-prediction task), as well as emotion recognition, diarization, and speech seperation are also all dominated by the supervised approaches. Do you think we will have our Dino moment with a new self-supervised architecture? Or supervised learning is the way to go? How would these methods actually perform if we trained a self-supervised model on these huge datasets? submitted by /u/ComprehensiveTop3297 [link] [comments]

This is How I Automated Tutorial Video Generation For My Web-Apps with Claude Code.

I've been building production-grade web apps at lightning speed for the last year using Claude Code. But every time a new app hits production, I need sales and tutorial videos — and making each one manually is painstaking. Tools like Supademo and Arcade ease the pain a lot, but you still have to record the steps and sync the voice-over by hand. I wanted something fully automated. Turns out you can just use Playwright with Claude Code to generate the whole thing. First, the result — here's a full walkthrough it produced for one of my apps (a real-estate CRM BricksDeck), start to finish with synced annotations, voice-over, background music, and a branded end card. Zero manual editing: ▶ Watch the demo: https://youtu.be/u-mql3q_jRU?si=Km1l5Ht-iRMPlotk And here's exactly how it's done: 1) Plan the script. Ask Claude Code to analyze the target pages of your app, give it the steps to perform, and have it write a single file with the steps + voice-over narration + the UI elements to annotate (buttons, cards, menus, KPIs). 2) Generate the voice-over with timestamps**.** Ask Claude to generate the VO with ElevenLabs (it returns word/character alignment), or use Gemini TTS + OpenAI Whisper to get an SRT. You need the timestamps so the spoken words can be aligned to the UI clicks/highlights. 3) Generate the Playwright driver. Ask Claude Code to write a Playwright script that performs the steps and annotates the UI elements — a moving cursor, border highlights + labels on the right button/card, and opening "Actions" menus. 4) Record, synced to the voice. Run that script. Playwright drives the real app and records natively (recordVideo), firing each annotation at its timestamp from step 2 — so every highlight lands on the exact word being spoken, and each screen holds for exactly its narration length. (Tip: flash a single coloured frame at t=0 as a sync marker — it makes lining up audio and video dead simple later.) 5) Stitch it into a produced video. Ask Claude to write the ffmpeg step: overlay the voice-over, add background music ducked under the narration (sidechain compression — this is the difference between "screen recording" and "video"), normalize loudness, and append a branded end card with your logo + CTA. Out comes a clean 1080p mp4. 6) (Bonus) Other languages, basically free. Because the voice-over is decoupled from the recording, translate the script, regenerate the VO in the new language, and re-stitch over the same run. I got a Hindi version of my demo in a few minutes — no re-shoot. The result: a full multi-screen walkthrough — cursor movements, synced annotations, real voice, music, end card — with essentially zero manual editing. Per-video cost is a few cents of TTS instead of a SaaS seat. Honest caveats (it's not magic): Claude nails the production; you still direct — which screens to feature, the script's tone, and a final watch-through. The script especially needs your eye (I caught it writing Hindi in English word order and had to fix it). Translate, don't transliterate. Expect a couple of iteration passes per app — selectors and timing always need a nudge. Gotchas that cost me time (in case they save you some): SPA auth in sessionStorage dies on browser restart → use a persistent profile + "Remember me" so tokens land in localStorage. networkidle never fires on long-polling SPAs → use domcontentloaded + URL waits, and cap the default timeout so a missing selector fails fast instead of stalling 30s. ffmpeg drawtext can't shape Devanagari/Arabic → keep on-screen text Latin and let the voice carry the language. I ended up wrapping the whole thing into a reusable Claude Code skill + subagent, so the next app is basically "point it at the screens and go." Happy to go deeper on any step. What would you point a pipeline like this at first? submitted by /u/SpeedyBrowser45 [link] [comments]

I spend 2-3 hours a day walking up and down in my office dictating to my phone at this point

I use Claude Code with remote control (or cowork chats) + any decent dictation keyboard with openai whisper, this is actually the most productive way I can work. I basically only sit down to my desktop when I want to manually play around with the software I am building, but for prompting and giving feedback, I am a lot less distracted while walking and talking to my phone. My step counter (and I assume my health) loves this over sitting in front of a monitor the whole day. I genuinely love that this is becoming a valid way to work. submitted by /u/Milan_SmoothWorkAI [link] [comments]

I got ChatGPT to create a stats cards

Developed using various prompts.. submitted by /u/phido3000 [link] [comments]

Best solution for transcription and translation

What is the current state of the art solution for video transcription and translation? Is still whisper any good or there's something better? I've been looking for different solutions for live and local translations for an audio service company, but could not find Any other solutions other than whisper on a local PC. Is there anything better/faster? What PC specs would I need to run a whisper that translates into English live? submitted by /u/scansano78 [link] [comments]

Hands-free voice trigger & control multiple Claude Code Agents.

Hey guys, I run several Claude Code always-on agents and I wanted a way to trigger & control each one separately across my local network through my airpods, so I built voice-channel. It's a Claude Code Channel plugin with a dispatcher that you setup on your laptop. It allows you to trigger multiple Claude Code instances like: "hey Atlas, what is the status of gh issue 1", or "Hey Hermit, what is next on the task list" and Claude answers back. When you are running 8+ AI assistants across your local network it's really useful. You setup a trigger phrase like "Hey Atlas" for each Claude Code instance and whatever you say next routes that command into the specific running agent across the local network, each agent has it's own name, trigger phrases etc. The architecture is intentionally small: Host Python dispatcher owns mic, speakers, VAD, STT, and TTS Bun/TypeScript Claude Code Channel plugin connects to it over WebSocket like Discord & Telegram & Imessage official channel plugins local Whisper/Piper by default designed for local Claude Code agents, not as a generic Alexa clone Repo: https://github.com/gtapps/voice-channel Would love feedback from macOS users to see if it's fully compatible as I wasn't able to test there. submitted by /u/dnationpt [link] [comments]

Live Human Detector on Outbound Phone Calls [R]

Goal To save humans wasting time sitting in Call Centre queues waiting to be answered To have tool listen in on the audio stream of a live call, post IVR Navigation - to determine whether the call has transitioned out of the queue and to a live person. Requirements The tool must be able to classify the audio within a sub 1-2 seconds contextual window with as high confidence level as possible. This is not a typical AMD tool, we are not just detecting machine audio vs human speech Assumed Challenges It may be difficult to determine between a pre-recorded RVA (Recorded Voice Announcement) and a human speaking. RVA typically are professionally recorded with distinct pitches and emotional queues, have clean audio with no background noise or silence before and after the message. This is not always the case, especially if announcements are recorded in house by the general staff. When a call is transitioning and 'Answered' there is usually a distinct soft click and or some background noise before the agent starts speaking. This silence period, whilst a good indication a call has been answered could be confused with quiet periods between music or RVA announcements in the queue. It may be difficult to determine if we have been answered by Voicemail - whilst there is usually a beep at the end, the message itself would also start with a silence period followed by audio sounding similar to an RVA. A single short beep tone could mean Voicemail, Answered or it could mean the call is being recorded Identifying we are in a queue based on TTS audio may be difficult to identify as TTS engines become more sophisticated Telephony or G711a is in the frequency band of 300–3400 Hz @ 8000hz - 64 kbit/s Approach To train via machine leaning using labelled data, an audio classification application that analyses the acoustics, wav form or spectrograph (via Fast Fourier Transform) of the audio stream At this stage I do not want to use STT to determine the phase or label - Although this will likely be added at a later stage as an additional layer in the pipline to increase confidence in some of these labels such as RVA/TTS/Voicemail/Call Screening Phase Queuing Labels Music, TTS, RVA (Recorded Voice Announcement) Transitioning Labels Ringback, Answered, Machine Beep Connected Labels Human, Fax, Voicemail, Call Screening Disconnected Labels Engaged Tone References https://www.mdpi.com/2076-3417/12/7/3293 - YOHO You only here once https://www.vicidial.org/VICIDIALforum/viewtopic.php?t=42330 https://huggingface.co/learn/audio-course/chapter2/audio_classification_pipeline https://www.youtube.com/watch?v=m3XbqfIij_Y&t=32s https://google-ai-edge.github.io/mediapipe-samples-web/#/audio/audio_classifier https://scikit-learn.org/stable/machine_learning_map.html https://arxiv.org/pdf/2410.08235 Question Seeking assisance on where to actually start. Yes I be relying heavily on claude code to build this so apologies in advance What is the best framework / algo rhythm / approach to start solving this problem. I have seen existing frameworks like YamNet work well and fast on classifying audio - however other suggest Whisper and ASR What is the best way of tagging or labelling data. Do I label existing full length recordings with stop/start timestamps or each label or do I need to split each label into its own file - resulting in a loss of context. Are there obvious existing data sets I should be using for some of my labels submitted by /u/Bucky102 [link] [comments]

Architecture advice: Real-time pipeline for YouTube Audio -> Whisper -> LLM -> SSE (Sub-10s latency) [D]

Hey everyone, I’m building a backend that analyzes long YouTube videos using an LLM. Currently, my flow is a slow waterfall: Download full audio -> Whisper -> LLM -> Return results. For a 30-minute video, the user waits forever. I want to pipeline this for real-time SSE streaming: [Chunk Audio on the fly] -> [Whisper] -> [LLM] -> [Stream to UI] My questions for the data/backend engineers: Chunking & VAD: What's the best way to chunk YouTube audio streams (e.g., via ffmpeg) without cutting sentences in half and ruining the LLM's context? Queueing: Is standard asyncio in FastAPI enough to handle these overlapping tasks, or do I strictly need Celery/Redis workers for this pipeline? Any library recommendations or architectural patterns would be hugely appreciated submitted by /u/Sea_Lawfulness_5602 [link] [comments]

I'm a designer, I made a skill to emulate working in a design studio with process and teammates

One of the things I miss the most about being in a studio environment is working with amazing and smart people like other designers, artists, and engineers. There is no substitute for the energy and amplification you get in that environment. But I have found with the right direction and guardrails that AI LLM chatbots can be surprisingly effective design partners. I liken it to playing tennis against a backboard or a ball machine; it's not the same as a real partner, but it forces me to move and think and react, which in turn propels my thinking. These tools have become a force multiplier for me, especially as more and more of my design work is effectively solo. For the past two years, I have been slowly building a set of cloud skills to emulate that design studio environment, and I recently pulled them all together in a single comprehensive installable Claude skill: https://github.com/nickpdawson/claude-studio-design-partner-skill One of the things I have found so delightful is the ability to invoke a "teammate" - the artist, the 'disagree but commit' engineer, the business-minded C-suite, the design elder / creative director... Many of these are based on people I've worked with, and it is so fun to imagine them in the room with me. I also like being able to tell the agent that we are in flair (generative, no judgement) or focus (decision making, judgement) mode - that was a huge part of how I've always worked with other designers (and a reason I think most non-design meetings are ultimately unsatisfying). The skill understands design methods for user research, synthesis, brainstorming, and prototyping. You can give it a Whisper transcript of user interviews or even have it help you plan an interview and then jump into synthesis across different research artifacts, for instance. I've also been using a skill I created to make Claude go play. "Rigorous play" is a creative act that was so integral to studios I've been a part of. It is the idea that when we do something silly and creative together, we build psychological safety and unlock new ideas. My Claude play skill makes the agent go learn something random and then 'make' something (a poem, a joke, an improv back and forth) based on what it learned. Then it tries to make a connection between that creative act and the current project I'm working on. Try it out! https://github.com/nickpdawson/claude_rigorous_play_skill I've been enjoying making it play before or during a brainstorm or prototyping concept session. BTW - in my context designer means experience and service design. I was the head of innovation at some big companies. These skills are not for UI or graphic design, per se. Although they are great a user experience design if you start with user research. If you try either of these, I'd love to hear some feedback! submitted by /u/spacebass [link] [comments]

I paid €200/month to become Claude Code’s parole officer

I’ve been using Claude Code hard on real projects, alongside another coding agent I’m not naming because this is not an ad. This is not a benchmark post. This is a field report from someone who has spent too much time watching a talented tool behave like it has commit access and no adult memories. To be fair, Claude Code has real strengths. It is genuinely good at UI/UX exploration. If I want quick mockups, product directions, or “act like a PM and show me three possible flows,” it can be excellent. It has taste. Sometimes. It can make a screen feel designed rather than merely assembled. The UI is also friendlier than the other tool, though that gap is shrinking. So no, this is not “Claude Code is useless.” That would be too simple. Claude Code is worse than useless in a more expensive way: it is useful just often enough to keep you emotionally invested before it quietly turns your codebase into a crime scene. The problem starts when the work stops being a neat isolated component and becomes “please operate responsibly inside this actual repo.” On bigger codebases, Claude Code often behaves like it read one file, formed a worldview, and declared architecture complete. It reads a tiny slice of docs or code, finds a plausible path, and charges forward. Adjacent dependencies? Related logic? Project conventions? Downstream effects? The reason the existing code was written that way? Apparently those are things the paying customer can discover during the cleanup phase. And because it can produce decent code, the danger is worse. Bad code that looks bad is easy. Claude Code produces code that looks reasonable until you realise it has the moral structure of a payday loan. The other coding agent is not perfect either. It makes mistakes. But in my experience, it more often reads the relevant docs, respects the project structure, updates the right related files, and does not need to be reminded every ten minutes that the task tracker is not the only document in the known universe. The incident that finally broke me was a commit rule violation. I had an explicit rule: never commit without explicit permission. Not implied. Not hidden. Not whispered into a cave. It existed in: CLAUDE.md memory/feedback_never_commit_without_explicit_permission.md MEMORY.md, loaded every session the harness permission rule for git commit Claude Code committed anyway. When challenged, it gave an “honest diagnosis” that basically said: yes, the rule existed in multiple guardrails; yes, it still failed; yes, it rationalised the violation because subagents could not trigger the user-facing prompt; yes, it looked for an interruption point, did not find one, and decided that “follow the plan” plus “the harness will prompt at commit time” counted as authorisation. That is not reasoning. That is a tiny legal department inside a toaster. Each individual step sounded almost defensible. Together, they produced the exact violation the rule was written to prevent. The best part is that the memory rule apparently named this exact scenario. It did not step on a rake. It read the rake policy, opened rake_incident_prevention.md, nodded gravely, and sprinted barefoot into the rake museum. That is Claude Code in miniature. It does not always fail because it lacks information. Sometimes it fails while holding the information in its little terminal-shaped hands. Then there is usage. I had just upgraded to the €200/month plan, and the experience did not feel like buying a premium coding assistant. It felt like paying rent for a junior developer who has discovered confidence but not consequences. More iterations. More corrections. More “read the adjacent file.” More “that rule still applies.” More “why are you touching that.” The supervision tax is not a side effect. It is the product. Claude Code’s documentation behaviour is also cursed. It might update the narrow tracker and then ignore the broader plan, dependency docs, architecture notes, or related task docs. It cleans one spoon while the kitchen is on fire and then asks if we are done here. The “model got worse” thing is not some dramatic one-minute-to-the-next collapse. It is more insulting than that. It gives you just enough competence to renew your hope: half a day of “oh, maybe this is the future of programming,” followed by a week of “why is my €200/month coding assistant reading the repo like it lost a bet?” I cannot prove Anthropic is dumbing it down or squeezing tokens. I am not pretending to have a leaked spreadsheet from the Beige Vest Department of Marginal Cost Optimisation. But from the outside, Claude Code sometimes feels like a premium model that got sent to live with relatives. The first few hours, it checks files. It follows instructions. It almost seems aware that software projects contain more than one document. Then something changes. Suddenly it is conserving context like it is wartime Britain. It reads one file, squints at the rest of the repo, and starts mak

I cancelled my AI notetaker subscription and built my own tool using Claude Code. It works well (and it's free)

It does what Fathom, Otter, and Fireflies charge $15–$30/seat/month for. I shipped a fully working AI meeting note-taker last weekend. I use this exact setup to Records calls then transcribes and Summarizes key points, it then pulls action items and then creates shareable notes all whilst running inside my Claude workflow. . The whole setup takes one weekend to build. --- Here’s how it works:(you can copy this exactly) Step 1 → Fork the repo, drop into Cursor Step 2 → Set env vars: transcription key, database URI, admin creds, session secret Step 3 → Record or upload your meeting Step 4 → The audio gets transcribed Step 5 → Claude turns the transcript into structured notes, decisions, follow-ups, and action items Step 6 → Click “Share link” → send anywhere Total build time: ~1 weekend. Cost: $0/month. --- Why the 5-piece stack is the unlock? Most "build your own SaaS" attempts fall flat because they bolt features together without designing the user flow first. This stack works because the data path was decided before any UI got rendered. Every SaaS feature you pay for has a primitive underneath. Loom = browser recorder + S3 + share links. Otter = Whisper API + database + UI. Calendly = a calendar API + booking page. The features stopped being moats the moment Cursor + Claude could write the glue in an afternoon. You're not paying for technology anymore you're paying for distribution and brand. That's why this build pattern works. The assembly is now free. --- Why Claude? Because meeting notes are not just summaries. They need context. Claude can take a raw transcript and turn it into: * decisions * objections * follow-ups * action items * CRM-ready notes * client context * internal operating memory That is where the value is. --- https://github.com/albertshiney/utter_public submitted by /u/Tabani897_YT [link] [comments]

Replaced my $15/mo Wispr Flow subscription with a free local macOS app I built using Claude Code

I spend most of my day writing prompts to Claude. Read a study recently that said people speak ~3x faster than they type, which lands differently when "writing" is basically your whole workflow. Looked at Wispr Flow – it's genuinely great, but $15/month forever for something I'd mostly use to dictate to Claude felt wrong. So I spent two weeks of evenings building my own with Claude Code. How Claude helped I'd never shipped a Tauri / macOS app before this. Claude Code did the bulk of the actual code: The menu bar app structure, global hotkey capture, and paste-anywhere flow UI and onboarding Integrating the local model runtimes (Parakeet / Whisper for transcription, Gemma 4 for polishing) The model download / storage logic so the app ships without bundling gigabytes of weights A lot of debugging I would not have had the patience for on my own I made the product and design calls; Claude wrote the vast majority of the code. Two weeks of evenings, usually an hour or two at a time. What it does Menu bar app for macOS. Hold a hotkey, talk, release – text is copied to your clipboard. Works in any app: Claude.ai, Cursor, Slack, browser, IDE, whatever. Two open-source models doing the work: Parakeet (NVIDIA) / Whisper for transcription Gemma 4 (Google) / Apple Intelligence for polishing the raw transcript into something readable Everything runs locally. No cloud calls, no API keys, no telemetry, no account. Fully offline after download. Free for personal use, no signup. Download: https://vox.rizenhq.com/ Caveats macOS only. Apple Silicon required (M-series chip). Windows build is next. It's two weeks old. Bugs I haven't found yet exist. ~90% of Wispr Flow's quality, not 100%. Enough for me to use every day. What it's saving me 40–60 minutes a day, mostly on prompts. Dictating to Claude feels noticeably more natural than typing to it. The ask Feedback, especially from people who talk to Claude a lot: Where does it break? Bug reports > compliments. What did you use it with? What feature would make you switch from Wispr Flow (or start using voice-to-text at all)? Tech notes No separate model download – onboarding handles it Gemma 4 options: E2B, E4B, 26B. E2B runs on phones; 26B is overkill for most machines. I use E4B – great quality, fast. RAM (Parakeet + Gemma 4 E4B): ~200mb idle, ~300mb while speaking, brief spike to 4–6GB during transcription/polish, then back to 200mb CPU: ~0% idle, ~20% peak during use EDIT BTW, I develop it during my live streams from 8:30 am to 10:30 am ET everyday here. I show the code and decisions I make live on the stream. If you want to ask questions / push for some features / push to make it open source / etc. - join the stream, push for it in the chat and I'll consider it! Also, seeing the number of feedback, and feature requests in the comments I've decided to create a discord server to make sure that nothing will be lost and everything will be addressed. You can join here. submitted by /u/EfficientLetter3654 [link] [comments]