tl dv alternative for ai developers
A tl dv alternative for ai developers who need usable output, not more cleanup
If tl dv records your meetings but you still rebuild prompts, ticket notes and billable hours by hand afterwards, speak that work where you already type and let Superscribe match it to the project.
Superscribe
Stop rebuilding calls from memory
Use Superscribe on a real phone call and leave with the transcript, summary, and suggested actions ready to review.
AI development moves too fast for cleanup steps. You speak prompts, test outputs, dictate project notes, and send client updates across a dozen tools. A tool like tl dv is great at recording a call and giving you a transcript, a summary and action items, but it does not track time, so the billable hours are still rebuilt by hand. It’s a solution for the recording part of the problem, not the whole workflow. You still have to stop creating to do admin.
If you’re looking for a tl dv alternative for AI developers, you’re likely feeling this pain. The real cost isn’t the meeting itself. It’s the time spent after, trying to reconstruct context and turn a conversation into usable, billable artifacts. The bottleneck is the manual step of turning raw transcription into structured work.
Try it on the real workflow
Turn spoken context into finished work
Use Superscribe to capture live prompts, notes, and client updates. The spoken words become project-matched notes, context, and billable time without a cleanup pass.
The Problem with Recap-First Workflows
For developers working with AI, the thinking and the doing are the same thing. A prompt isn’t a pre-meeting note. It’s the work itself. Implementation notes happen in the moment, not in a summary written hours later. When your workflow is live, a tool that just records and transcribes is already a step behind.
The core issues with a recap-first approach are:
- Lost Context: The nuance of why you wrote a certain prompt or made a specific architectural decision is gone by the time you sit down to write a summary.
- Admin Overhead: You have to pause your primary work to process transcripts. This is a context switch that kills momentum.
- Inaccurate Time Tracking: Timers are a guess. Trying to match a transcript to a block of time on a timesheet is reconstructive work, not accurate capture.
You need a system that captures the work as it happens, not one that gives you homework for later.
A tl dv alternative for AI developers who ship, not summarize
Superscribe is built on a different principle: capture the work at the source. Instead of recording a call and then processing it, Superscribe lets you dictate prompts, notes, tickets, and updates directly where you work. The act of speaking is the event.
As you dictate, Superscribe captures the transcription, semantically matches it to the right project, and tracks time in the background. There is no summary step. The output is structured, project-matched, and billable from the start.
This is a subtle but critical shift. It moves the capture point from post-event recap to live-event dictation.
| Feature | tl dv | Superscribe |
|---|---|---|
| Primary Workflow | Records meetings, with or without a bot, and summarizes them | Captures live dictation during any task |
| Output | Transcript, AI summary and action items | Structured, project-matched text |
| Time Tracking | No time tracking features listed | Automatic, based on active dictation |
| Best For | Recording Zoom, Google Meet and Teams meetings | Capturing in-progress work and context |
Try the workflow
Speak the prompt where you already type
Dictate a prompt into Cursor, a note into a Linear ticket or an update into Slack. Superscribe types the words, matches them to the project, and tracks the time.
From a Personal Pain Point to a Practical Tool
I built Superscribe because I was tired of guessing my hours. At the end of the month, I’d stare at my calendar, Git logs, and Slack messages, trying to piece together a timesheet. I knew the numbers were wrong and I was losing money. The admin work was a constant drag on the real work.
Three years ago, I had an idea for an app to automatically capture client calls. It seemed too complex, so I shelved it. I spent the next few years building other voice tools, and each one taught me something new. The real breakthrough came when I added automatic time tracking to my main desktop app. I realized the missing piece wasn’t just about calls. It was about capturing all spoken work.
The answer became clear. New AI tools made the original idea practical. The proof came on a flight. I used the plane’s Starlink Wi-Fi to make normal business calls with my regular phone number. The calls were transcribed, cleaned up, turned into structured notes, and sent straight into my project system. Agents handled the next steps without me lifting a finger.
This is the tool I always wanted. You speak a prompt or a note. Clean words appear. The time and project context are handled in the background. No timers. No guessing. It’s for anyone who wants to stay in creation mode instead of doing paperwork.
How It Works in Practice
Imagine you’re working on a new feature.
- You speak a prompt into Cursor: “Okay, refactor the user authentication service to use the new session manager. Add error handling for token expiration and database connection failures.” Superscribe captures this, matches it to the ‘auth-feature’ project, and logs the time.
- You dictate a note into a Linear ticket: “Note for the team. The new session manager requires a database migration. I’ve added the script to the repo. Make sure to run it before deploying.” This is captured and associated with the same project.
- You send a client update in Slack: You dictate a message. “Just a quick update. The new authentication flow is implemented and passing all tests. I’m moving on to the UI integration now.” This too is logged as billable communication.
At the end of the day, you have a complete, accurate record of your work, broken down by project, without ever starting a timer or writing a summary. Your Git commits provide supporting context, but the primary record comes from the voice layer where the thinking and planning happened.
See it in action
Test this on your next task
Use Superscribe to dictate the notes and context for your next coding task. See how the spoken words become a structured, billable record of the work.
Frequently Asked Questions
Does this integrate with my coding tools like Cursor or VS Code? Superscribe works as a layer on top of your existing tools. It functions as a global dictation input. Wherever you can type, you can speak. This means it works with any IDE, text editor, or browser-based tool without needing a specific integration.
How does it know which project I’m working on? Superscribe uses semantic matching. It compares what you dictate with your projects, by keywords and by meaning, to associate your work with the correct project. The more you use it for a specific project, the more accurate it becomes.
Is this just for calls or for my solo work too? It was designed for solo work first. The core product is about capturing your dictated prompts, notes, and thoughts while you are actively working. The ability to capture phone calls uses the same engine, providing a unified way to track all your billable communication, whether it’s with a client or with yourself.