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meerab

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https://videotobe.com https://PresenterPrep.com https://pitchdecklive.com

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Congrats on shipping this!

I went down the same path for videotobe.com, fully client-side with ffmpeg.wasm, and it fell over on longer videos. The memory ceiling and encode times pushed me to a cloud processing pipeline.

You've managed to solve both, the WebCodecs plus Pixi plus ffmpeg.wasm split looks like the right decomposition in hindsight.I processed 3+ hrs of media using VidStudio and it held up. Nicely done!

You stop paying for ads and the product link disappears. Reason why founders tend to go for reddit is because it gets indexed by Google and LLMs and the link gets 'preserved'.

What is your experience in stickiness of users after acquisition via Ad? Given crack down by reddit mods for posting links - I am considering just buying ads.

We run a similar setup in production at VideoToBe – FFmpeg inside Cloudflare Containers with R2 mounted as a local filesystem via FUSE (tigrisfs).

On the debate about Cloudflare vs Fly vs spot instances – for bursty, infrequent workloads that already use R2 for storage, Containers are a natural fit.

The container accesses R2 over Cloudflare's internal network, so files never hit the public internet.

Check out our implementation - https://videotobe.com/blog/how-we-process-video-with-cloudfl...

(The main difference is we don't use Cloudflare Queues in conjunction with Cloudflare Containers. You can set max_instances to your desired settings to process parallel requests.)

Layoffs at Block 5 months ago

Spot on. This tweet will go down in history — right next to Jack's first tweet. We all understand businesses require difficult decisions. This is one of the best layoff letters I've seen. Short, honest, and human.

This is a greatly researched article, and the site is just awesome!

Kanchipuram Saree has a rich history, and I learned so much more by reading this article.

I am intrigued by the Kanchipuram saree and have dreamed about owning one. The digital ledger is a unique idea – if authenticity is established, it would be easy to invest in this as art piece.

I'm working on VideoToBe — a audio and video Transcription service with an AI chat layer. Whisper for transcription, then you can chat with your recordings instead of rewatching.

Also building simple tools for simple task such as converting audio-to-video to post on youtube and more for business users who find Premiere/Final Cut overkill for basic tasks. videotobe.com

I use a two-pass approach - first pass with ASR (OpenAI Whisper) and second pass with an LLM. I ask users to provide context upfront and use that as the "initial_prompt" parameter in Whisper: https://github.com/openai/whisper/discussions/963#discussion...

Gemini might have similar capabilities for custom vocabulary, though I'm not certain about their specific implementation. The two-pass ASR+LLM approach could work with Gemini's output as well.

My actual time breakdown as a solo developer:

10% Daily time to learn about various AI tools and improve my workflow.

20% Procrastination (this might be way more than what I'm willing to accept. But this is HN, I want to appear smart )

20% Writing detailed description of features and breaking down task lists, writing acceptance tests.

20% AI Coding (Claude Code)

20% Testing + Production

Personal opinion:

Claude code is more user friendly than cursor with its CLI like interface. The file modifications are easy to view and it automatically runs psql, cd, ls , grep command. Output of the commands is shown in more user friendly fashion. Agents and MCPs are easy to organized and used.

Yes. /init will initialize the project and save initial project information and preference.

Ask Claude to update the preference and document the moment you realize that claude has deviated away from the path.

I am working on VideoToBe.com - and my stack is NextJS, Postgresql and FastAPI.

Claude code is amazing at producing code for this stack. It does excellent job at outputting ffmpeg, curl commands, linux shell script etc.

I have written detailed project plan and feature plan in MarkDown - and Claude has no trouble understanding the instructions.

I am curious - what is your usecase?

I have barely written any code since my switch to Claude Code! It's the best thing since sliced bread!

Here's what works for me:

- Detailed claude.md containing overall information about the project.

- Anytime Claude chooses a different route that's not my preferred route - ask my preference to be saved in global memory.

- Detailed planning documentation for each feature - Describe high-level functionality.

- As I develop the feature, add documentation with database schema, sample records, sample JSON responses, API endpoints used, test scripts.

- MCP, MCP, MCP! Playwright is a game changer

The more context you give upfront, the less back-and-forth you need. It's been absolutely transformative for my productivity.

Thank you Claude Code team!

Your routine seems completely normal. This is Not Youtube addiction and procrastination. You work 8 hours - that's already your productivity. The idea that your personal time also needs to be optimized and highly productive is tech bro nonsense.

After a full workday, your brain wants easy dopamine. That is being human. You're not a startup. The people posting about 5am routines are just performing productivity online.

Maybe go to bed earlier or be more mindful when it comes to digital dooms scroll.

Bingo - that is the real reason!

If an Agentic requests originate from a server - it is easy for receivers to block the server. Write a browser-plugin and you are playing in somebody else's garden.

Write a browser and now you get to pretend like a human!

YT transcripts definitely lack speaker ID. LLMs can infer speakers from context but miss nuance without proper speaker recognition.

I have been tackling this while building VideoToBe.com. My current pipeline is Download Video -> Whisper Transcription with diarization -> Replace speaker tags with AI generated speaker ID + human fallback.

Reliable ML speaker identification is still surprisingly hard. For podcast summarization, speaker ID is a game-changer vs basic YT transcripts.

Interesting approach to transcript generation!

I'm implementing a similar workflow for VideoToBe.com

My Current Pipeline:

Media Extraction - yt-dlp for reliable video/audio downloads Local Transcription - OpenAI Whisper running on my own hardware (no API costs) Storage & UI - Transcripts stored in S3 with a custom web interface for viewing

Y Combinator playlist https://videotobe.com/play/playlist/ycombinator

and Andrej's talk is https://videotobe.com/play/youtube/LCEmiRjPEtQ

After reading your blog post, I will be testing effect on speeding audio for locally-hosted Whisper models. Running Whisper locally eliminates the ongoing cost concerns since my infrastructure is already a sunk cost. Speeding audio could be an interesting performance enhancement to explore!