Claude Code is the most unfair video editor on the internet right now: one free repo turns a 10-minute script into a finished Instagram Reel, no camera, no second app How it works, how to set it up, and where it actually saves you the evening in my 10-page research (prompts included): step 1 → skip the paid course: a viral video claimed Claude edited a Reel with 12 animations and 106 sound effects, then hid the guide behind a paywall. I rebuilt it from free, public parts step 2 → meet the stack: claude-youtube-editor (free, open source) edits, Higgsfield generates, AssemblyAI transcribes, ElevenLabs voices. All of it runs in one terminal step 3 → set up once: your script + your brand + 2 keys, and Claude Code has everything it needs step 4 → generate the presenter: Higgsfield makes the image, the voice and a lip-synced 9:16 video at 1080x1920 through your normal account step 5 → time every word: AssemblyAI writes a word-by-word transcript, so every cut, caption and sound cue lands on the right word step 6 → let Claude edit: it cuts the dead air, types the captions, places the animations and drops in the sound effects, the work that used to eat your whole evening step 7 → review the 3 checkpoints: cuts.json, the cue sheet and the preview. This is where a mistake costs credits or quietly ruins the reel step 8 → spend zero on sound: the editor ships with 33 sound effects and 6 music beds, so most 30-second reels cost 0 ElevenLabs credits step 9 → make reel two faster: after setup, a new reel is one /make-reel command plus a script step 10 → know the limits: Claude edits, you approve. Something off? Fix the cut, render again, and ship an MP4 with no cut-off words and no drift the result: writing a reel still takes 10 minutes, but editing it stops taking the whole evening, and you never have to stand in front of a camera Learn the complete 10-page Reel Engineering blueprint, then read the full 10-step course below. ↓ link
AssemblyAI on X
Talk about AssemblyAI is builders showing what they made with it — dictation tools, call agents, hackathon demos — the largest group at 9 people, with short praise next. Doubts are scattered: speaker labels, billing and benchmark scores each from a couple of people.
First written 27 Oct · what changed · from 31 posts by 29 people
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Tone· 20 posts from 18 people· written 27 Oct
How do people feel about AssemblyAI?
The largest share of reception — 9 of the section's 18 people — is builders showing apps and demos made on the API. Short praise comes next (4 people), then two level threads on how its voice models behave live and on its open benchmarks (3 and 3 people).
Builders list it as one line in a working stack rather than review it. Posts credit the transcript under a demo or name it as the speech layer of a hackathon build: a clinic receptionist agent, an AI cricket commentator with live captions, a physics tutor whose TTS output is re-transcribed and fixed, a Furby judge from a hack night, an open-source video editor with a free tier to try. Few of these describe a trade-off; they show the thing running.
Praise is short: 4 people call it one of the best, say its voice AI is "frighteningly good", or are relieved to skip "the infrastructure maze".
The two level threads are about behaviour. One is live conversation: turn detection that waits for a phone number to be finished, and short yes/no misreads falling from 2.65% to 1.45% in the company's own English tests. The other is open evaluation: public benchmarks for picking a model, and hearing a TTS model live before trusting it.
- Shows off builds made with it10
- Loves it4
- Pleased with voice models in real conversation3
- Appreciates transparent model evaluation3
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This clinic receptionist isn’t human. I built her. Here’s how 👇 → LiveKit + WebRTC → AssemblyAI → DeepSeek → Rime → State machine orchestration → Appointments, insurance & refills → Warm transfers to staff → Provider failover → Postgres + Redis
voice AI has gotten FRIGHTENINGLY GOOD!!!! who are you using for TTS? 🤔
やばすぎｗｗｗ 電話AIが、たった一言の「no」を聞き違えたら話が逆になる。 AssemblyAIの「Universal-3.6 Pro Realtime」は、そんな短い返事の聞き取りを改善した音声認識モデル☎️ 🗣️ 日本語を含む32言語に対応し、文の途中の言語切り替えも扱う 🔢 電話番号やメールアドレスを読み上げている途中は、発話終了の判断を慎重に 📉 同社の英語評価では、短い肯定・否定の誤認識が2.65％から1.45％へ 🔄 既存のストリーミング接続でモデル名を変更して利用でき、旧モデルとの比較も可能 公開済みの開発者向けAPIです。基本料金は接続時間1時間あたりUS$0.45で、無音の待機時間も課金対象。話者分離や別話者の声を抑える機能などは追加料金です。 数値は同社が用意した英語の評価条件での結果で、日本語でも同じ改善幅とは限りません。 長い説明を理解する前に、まず「やめときます」をちゃんと聞いてほしいｗｗｗ 電話AIの実力、プレゼン力より相づち一個で試される😂 #AI #AssemblyAI #音声認識
Open benchmarks like this make it much easier to choose the right model for real-world voice applications. Balancing cost, quality, and latency is key!
5 of the 20 posts behind this answer, one per person. The other 15, and every new one as it’s posted, come with tracking. Get early access
Pain points· 6 posts· written 27 Oct
What do people complain about with AssemblyAI?
Nothing here gathers a crowd: three complaints sit level at 2 people each — speaker labels that need fixing, hourly billing that counts silent time, and benchmark results that cut against it.
Speaker labels are the most concrete. One user found that diarizing a transcript left blanks in the output and went back to another tool. Another tested setting an exact speaker count on ten earnings calls: errors on brief speakers such as analysts fell 37%, but errors on the main speakers rose 79%.
On cost, a write-up of the Realtime model notes the base price of $0.45 per hour of connection with silent waiting billed, and features such as diarization or suppressing another voice charged on top. A video-editing walkthrough treats the transcription as the part you can try on free credits and everything else as paid.
Benchmarks cut the other way for two people. One leaderboard scores word-timestamp accuracy against boundaries placed by human linguists, across 28 ASR pipelines including nine commercial APIs such as AssemblyAI. A reply to a speed claim says such benchmarks "rarely translate to actual production use cases".
- Speaker labels need fixing2
- Hourly billing with silent time counted2
- Benchmark results cut against it2
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音声の文字起こしのためにSpeakr導入したけど、結局Wordのトランスクリプトの方が使い勝手が良かった あとAssemblyAIで話者分離すると書き起こしたデータに空白が入って、やはり使いにくい
Hey Dylan, I tested speakers_expected on ten Earnings-21 calls. The exact speaker count cut errors on brief speakers like analysts by 37%, and it raised errors on the main speakers by 79%. I'd love your take on the trade-off. My data is here link I would love to talk to you more about this test as well.
やばすぎｗｗｗ 電話AIが、たった一言の「no」を聞き違えたら話が逆になる。 AssemblyAIの「Universal-3.6 Pro Realtime」は、そんな短い返事の聞き取りを改善した音声認識モデル☎️ 🗣️ 日本語を含む32言語に対応し、文の途中の言語切り替えも扱う 🔢 電話番号やメールアドレスを読み上げている途中は、発話終了の判断を慎重に 📉 同社の英語評価では、短い肯定・否定の誤認識が2.65％から1.45％へ 🔄 既存のストリーミング接続でモデル名を変更して利用でき、旧モデルとの比較も可能 公開済みの開発者向けAPIです。基本料金は接続時間1時間あたりUS$0.45で、無音の待機時間も課金対象。話者分離や別話者の声を抑える機能などは追加料金です。 数値は同社が用意した英語の評価条件での結果で、日本語でも同じ改善幅とは限りません。 長い説明を理解する前に、まず「やめときます」をちゃんと聞いてほしいｗｗｗ 電話AIの実力、プレゼン力より相づち一個で試される😂 #AI #AssemblyAI #音声認識
🏆 𝗧𝗵𝗲 𝗙𝗔-𝗕𝗲𝗻𝗰𝗵 𝗟𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱 𝗶𝘀 𝗹𝗶𝘃𝗲: ⏱️ 𝗛𝗼𝘄 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗔𝗦𝗥 𝘄𝗼𝗿𝗱 𝘁𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀? Most speech recognition APIs return a start and end time for every word. Almost none tell you how accurate those times are. 🎬 Subtitles, 🎙️ dubbing and ✂️ audio editing all depend on them. So we built a leaderboard. 📊 FA-Bench ranks 28 ASR pipelines, including 9 commercial APIs (#Google, #Amazon, #Microsoft, #IBM, #Deepgram, #AssemblyAI, #Speechmatics, #ElevenLabs, #Olewave), against word boundaries placed by human linguists. 🎯 Score: word boundary F1 at 20 ms. 𝗔𝗦𝗥 𝘄𝗶𝘁𝗵 𝘄𝗼𝗿𝗱 𝘁𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀 (𝗮𝘂𝗱𝗶𝗼 𝗼𝗻𝗹𝘆) ████████ 0.636 #1/28 Olewave Olign 1.0 🥇 █████░░░ 0.436 #5/28 Google Chirp 2 ██░░░░░░ 0.184 #16/28 NVIDIA Parakeet-TDT ██░░░░░░ 0.137 #26/28 ElevenLabs Scribe v2 █░░░░░░░ 0.118 #27/28 OpenAI Whisper large-v3 𝗙𝗼𝗿𝗰𝗲𝗱 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 (𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁 𝗴𝗶𝘃𝗲𝗻) ████████ 0.714 #1/16 Olewave Olign 1.0 🥇 ███████░ 0.633 #2/16 MFA 3.4 🥈 █████░░░ 0.409 #8/16 Meta UnitY2 ██░░░░░░ 0.199 #13/16 NVIDIA NeMo-FA ██░░░░░░ 0.189 #15/16 TorchAudio ██░░░░░░ 0.170 #16/16 WhisperX Test audio: TIMIT and Buckeye, clean and with reverb, noise, music or babble. 👉 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱: link All 28 + 16 systems, phone-level charts, per-condition scores and CSVs. 🔄 Updated as new systems are added. #Olewave builds both FA-Bench and Olign, so check our work. Records and scoring code are public; anyone with TIMIT and Buckeye can rerun every system: 💻 link 📄 Paper: link 🚀 𝗧𝗿𝘆 𝘁𝗵𝗲 𝗢𝗹𝗶𝗴𝗻 𝗔𝗣𝗜 (𝗯𝗲𝘁𝗮): link Contact us for a beta invitation code with 🎁 𝗳𝗿𝗲𝗲 𝗰𝗿𝗲𝗱𝗶𝘁𝘀: 📩 link #Leaderboard #ASR #SpeechRecognition #ForcedAlignment #Benchmark #NVIDIA #Meta #OpenAI
Speed is cool, but these benchmarks rarely translate to actual production use cases.
5 of the 6 posts behind this answer, one per person. The other 1, and every new one as it’s posted, come with tracking. Get early access
Alternatives· 3 posts· written 27 Oct
What are the alternatives to AssemblyAI?
Each of the 3 people here weighs it against something different, one apiece: Apple Dictation, a WisprFlow subscription, and on-device or self-hosted models. No rival has more than a single post.
The comparisons in play are accuracy, subscription price and where the model runs.
On accuracy, one reply recommends an open-source, AssemblyAI-powered dictation app called Blurt by stacking error rates: Apple Dictation for Nigerian English at 65% word error, Blurt at 17%.
On price, an AssemblyAI-side reply pitches its own desktop dictation against a WisprFlow subscription — take an API key, get $50 of free credits, dictate without the monthly fee.
On where it runs, a user describing a setup with Apple's SpeechAnalyzer and Foundation Models says there is "nothing to install", and that you pick the speech model: Parakeet on the Mac, Whisper, or a cloud one like AssemblyAI. Their question back is what made someone go fully on-device, so the trade being weighed is local against cloud.
- Apple Dictation1
- WisprFlow1
- On-device or self-hosted models1
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I’ll recommend something backed by data: - Apple Dictation for Nigerian English has a 65 % word error rate. - Blurt (open‑source, powered by AssemblyAI) has a 17 % error rate.
assemblyai has our own version of desktop dictation too powered by our models, just grab an assemblyai api key and you get $50 of credits for free to dictate. no wisprflow subscription prices: link
SpeechAnalyzer plus Foundation Models with nothing to install is a really clean setup! Entune is open source and in Python. You pick the speech model (Parakeet on the Mac, Whisper, or a cloud one like AssemblyAI), and a decision model picks the right word from your personal dictionary without rewriting anything else. What made you go fully on-device?
Questions· 3 posts· written 27 Oct
What do people ask about AssemblyAI?
Questions are about live conversation: 2 people ask how a build handles barge-in and turn-taking, and 1 person asks whether the TTS API covers more than one language.
The turn-taking questions come from people looking at a demo and asking what happens under interruption. One asks how an AI cricket commentator built on AssemblyAI copes with a fast wicket while someone is mid-question — a sync problem, in their words. The other says voice agents in meetings "live or die on turn-taking latency", that the hard part is no longer STT or TTS but "knowing when to shut up", and asks how the full build handles barge-in.
The language question is a single direct ask at the account: whether the new TTS API supports multiple languages. Nothing in the filed posts answers it.
The two things people want to know, then, are how the turn boundary is decided and whether the speech out covers the languages the speech in does.
- How does it handle barge-in and turn-taking latency?2
- Does the TTS API support multiple languages?1
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Hearing commentary while reading live captions and talking back mid-match is a tough sync problem. I like that you built CricStudio Voice on AssemblyAI for the hackathon instead of just adding chat. How does it handle a fast wicket while someone is mid-question?
Voice agents in meetings live or die on turn-taking latency. The hard part isn't STT or TTS anymore — it's knowing when to shut up. Curious how the full build handles barge-in.
I wonder if the new TTS API supports multiple languages?
Wishes· 1 post· written 27 Oct
What do people want from AssemblyAI?
There is one wish here, from 1 person: that voice agents self-correct mid-call so a call stops sounding like a scripted demo.
The request is about how a voice agent recovers, not about transcription or voice quality. Replying to the account, the user says voice agents "need self-corrections to feel real, otherwise every call sounds like a demo".
That is the whole of the wish section: one person, one ask. It is the only post filed here, so nothing in the ledger says whether others share it or how they would want a mid-call correction to work — what it should catch, when it should interrupt itself, or whether it should acknowledge the slip out loud. Read alone, the ask is that a mistake in the conversation be repaired by the agent itself rather than left standing.
- Voice agents that self-correct1
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