Rev AI, part of the Rev family, is a developer-first API that delivers industry-leading accuracy and fast performance at global scale. Click to learn
Get machine-generated transcripts in minutes from pre-recorded files or in real-time as audio streams. High accuracy across 57+ languages with proper grammar, punctuation, and formatting. Rev AI consistently outperforms competitors in accuracy for virtually every use case. Our proprietary models are trained using a carefully selected subset from a library of over 7M hours of human-verified speech data, giving us unmatched precision and adaptability. Get up and running in under an hour with our easy-to-use API, comprehensive SDKs, and expert support. Deploy in the cloud or on-prem. Go beyond transcription with language identification, sentiment analysis, topic extraction, summarization, and translation. Turn voice content into actionable intelligence. Handle sensitive data with confidence. SOC II, HIPAA, GDPR, and PCI compliant with 99.99% uptime. All files encrypted at rest and in transit. Enhance content searchability and analysis with precise word-level timestamps. Perfect for media applications, accessibility, and content indexing. Serve customers worldwide with 57+ languages and context-aware translations. Meet demand in new markets with consistently low WER. Find the right solution for you For press inquiries, email us at: press@rev.com Find the right solution for you For press inquiries, email us at: press@rev.com This short tutorial will teach you the basics of using the Asynchronous Speech-to-Text API. It demonstrates how to produce a transcript of an audio file submitted by you. This tutorial assumes that you have a Rev AI account. If not, sign up for a free account. The first step is to generate an access token, which will enable access to the Rev AI APIs. Follow these steps: The new access token will be generated and displayed on the screen. Save your access tokens somewhere safe; you will only be able to see them once. You are allowed a maximum of 2 access tokens at a time. Submit an audio file for transcription to Rev AI using the command below. Replace the REVAI_ACCESS_TOKEN placeholder with the access token obtained in Step 1, and replace the sample file URL shown below with the URL to your own audio file if required. You'll receive a response like this: You now need to wait for the job to complete. Wait for approximately 1 minute and then check the status of your job by querying the API as shown below: Polling the API periodically for job status is NOT recommended in a production server. Rather, use webhooks to asynchronously receive notifications once the transcription job completes. Here is an example of the output: Alternatively, you can get the plaintext version by running the command below:
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Pricing found: $0.20, $0.10, $0.30, $0.005, $0.005
Improving Code Quality with pre-commit
I write a ton of Go and Java code using Claude Code and most often it recommends packages etc that are out of date, and vulnerable. One approach I have is to use pre-commit, and then create a .pre-commit-config.yaml in the root of the project looking something like this: --- repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-merge-conflict - repo: https://github.com/golangci/golangci-lint rev: v1.64.0 hooks: - id: golangci-lint args: [--timeout=5m] - repo: https://github.com/igorshubovych/markdownlint-cli rev: v0.43.0 hooks: - id: markdownlint - repo: https://github.com/adrienverge/yamllint rev: v1.35.1 hooks: - id: yamllint - repo: local hooks: - id: vale-sync name: vale sync (download styles) language: system entry: vale sync pass_filenames: false always_run: true - repo: https://github.com/errata-ai/vale rev: v3.10.0 hooks: - id: vale args: [--config=.vale.ini] - repo: local hooks: - id: govulncheck name: govulncheck language: system entry: govulncheck ./... pass_filenames: false types: [go] - id: go-test name: go test -short language: system entry: go test -short ./... pass_filenames: false types: [go] For Java, Python, .NET etc you'll need to update it with the respective tools. The short story is to always upgrade your packages (on your default branch; perhaps not on a release branch), perform vulnerability scans, and basic quality checks before commit. Claude Code suggests code from its training which lacks security rigor or has vulnerabilities. The commit fails, which allows Claude Code to detect the issues and fix it. Unlike Claude Hooks, we're not getting in the way of it editing files etc. and thus saving tokens. I found that skills etc makes no material impact on quality; its it or miss. You can install pre-commit on your machine on macOS brew install pre-commit or via pip (or pip3 depending on your host) pip install pre-commit Then setup a global directory for git-hooks pre-commit init-templatedir ~/.git-template git config --global init.templateDir ~/.git-template So whenever you clone a repository or create a new one, pre-commit will be invoked and if there's a configuration file, it will run. In the case of Go, I use golangci-lint, which ensures the Go code meets a certain quality, which is almost always better than what Claude Code produces. You can also use act to test GitHub Actions, and actlint to make sure Claude produces reasonable GitHub Actions etc. Vale is used to fix my Suf Efrikan English from time to time, trying to keep it simple, free of jargon etc. It also helps Claude with its writing, especially when the audience speaks different flavors of English, or English is a distant 3rd language. Another tool to incorporate in your pre-commit configuration is checkov, which will not only catch IaC issues, but also catch stuff related to GitHub Actions and whatnot. This helps Claude Code to produce more secure code and configurations, rather than the slop one would find on the internet. For Go, I also use a make file to control what actions Claude can take, and then deny all go commands, redirecting Claude Code to use the makefile. This prevents the case where Claude Code creates binaries all over the place, and whatnot. It also forces Claude when it wants to build the code to go through security scanning and vulnerability management (which also happens during commit) to address any issues. If you use Java with Maven for example, you can integrate these checks into Maven such that mvn clean verify behaves the same way as the Makefile, ensuring we do vulnerability checks, security scans and whatnot. Better yet, ask Claude Code to generate the necessary configurations etc, test it out, and tell it your preferences. I found that this is far more effective that adding Claude hooks to format, scan code and whatnot. My token usage is also much lower it seems. And it also helps when I work on the codebase. This old fart can't always keep up with all the latest packages and whatnot. And naturally, you'll do your own review, as well as ask an AI assistant to perform more reviews. This works regardless of the AI assistant you're using or the model you're using. Even Opus 4.6 generates insecure and vulnerable code based on its training. Its not a silver bullet (anyone old enough to remember that paper?), but it will nudge Claude Code in the right direction. submitted by /u/bloudraak [link] [comments]
View originalSalesforce to Excel automation
Hello, Our company has bought in fully to OpenAI (we all have enterprise licenses now). I work in FP&A (rev ops). Most of our rev ops data comes from our CRM (Salesforce). Codex has done great modeling, but currently - I still have to export data (using the SF XL connector) before feeding it to the model. Is there a better way of doing this? I would love to further automate my pricing/churn/etc. Data. Codex with a rock solid prompt is still pretty easy, but I want to push things further if possible. submitted by /u/CravenMoorehead143 [link] [comments]
View originalUsing Claude to Help a Small Business Modernize
I work in financial services and have no real experience with CS or Coding but recently have been intrigued by the capabilities that AI and AI Agents has to offer. Specifically the ability to create and build systems with little technical skills. Don’t get me wrong I consider myself intuitive and a fast learner and tech savvy. I am also completely willing to learn to code just never got in to it. And ingesting thing I have observed in both corporate and my personal circle is that most just use AI a glorified Google meanwhile I find so much more potential in it. I feel like companies and people are just spraying and praying when it comes to AI due to the hype and very few are actually seeing broad benefits especially when it comes to corporations and businesses ROIs. Yet I think we are in an era where we could definitely help bridge this gap and disconnect between people who are using AI to automate and build and those who are just asking it what they should eat for breakfast tmrw lol. Anyways I went to a spa recently and although their services are good they have awful CRM and book everything via WhatsApp text messages. They sell spa treatment packages and barely keep track of how many sessions one has had and most of their clients are word of mouth. Although I know there are CRMs and booking services out there I asked if I could help build a bespoke system for them. I have no experience with building with AI or coding but thought this would be a great first project and possibly create an agency out of it making some extra money. Does anyone have any experience with this sort of work, what is a good way to get started, how do you usually price these things (can you make it a reoccurring rev business?) Would really appreciate some guidance and feedback, thank you guys! submitted by /u/brandonvz808 [link] [comments]
View originalかんなちゃん天使 http://t.co/WynRxjqNf8
かんなちゃん天使 http://t.co/WynRxjqNf8
View originalさやかりふらわーとオタ活してます♡ なぎさ推しです！ Twitter始めました(^-^)♡
さやかりふらわーとオタ活してます♡ なぎさ推しです！ Twitter始めました(^-^)♡
View originalYes, Rev AI offers a free tier. Pricing found: $0.20, $0.10, $0.30, $0.005, $0.005
Key features include: Speech-to-Text, Lowest Word Error Rate (WER), Least Biased, Developer-Friendly Integration, AI Insights, Enterprise-Grade Security, Forced Alignment / Precision Timestamps, Global Language Coverage.
Rev AI is commonly used for: Speech-to-Text.
Based on user reviews and social mentions, the most common pain points are: token usage.
Based on 14 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.
