PodBite

Inspiration

Podcasts are long, and finding specific insights or moments often feels like searching for a needle in a haystack. We wanted to build a tool that makes podcasts as searchable & skimmable as blogs or YouTube chapters.

What it does

Podbite lets users paste a podcast URL and describe the part they’re looking for (e.g., “talking about AI and the future of work”). It then finds the exact timestamp in the episode and gives you a direct link to jump right to that moment.

How we built it

We built Podbite using Python. We integrated podcast URLs to fetch audio, generated transcripts with speech to text, and used embeddings to match user queries with precise timestamps. The frontend was designed for simplicity just paste and search while optimizing performance for longer episodes.

Challenges we ran into

Render issues: We spent late nights debugging annoying rendering problems in the frontend. Accurate timestamping: Aligning transcript segments with podcast time codes was tricky. Noisy audio: Some podcasts had poor audio quality, which reduced transcription accuracy. Semantic mismatch: Users sometimes typed abstract queries that didn’t exactly match the transcript. Handling long files: Long podcasts took time to process efficiently.

Accomplishments that we're proud of

Shipped Quickly Nailed the UX, no login, just search & get results Early testers loved it and found it surprisingly accurate. Posted on X & got users!

What we learned

Audio interfaces are still clunky compared to text or video. Good transcripts and embeddings go a long way in solving discoverability.

What's next for PodBite

Support for multi-segment queries and auto clip generation. Chrome extension and mobile app. Personal podcast search history. Partnering with podcasters to offer native chaptering and summaries.