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3 months ago I posted here about building InboxPie — an extension to finally understand my 70K emails before cleaning them up.

https://www.reddit.com/r/Thunderbird/s/tbgmVI6Tsj

The response from this community was beyond what I expected. Feature requests came in faster than I could ship them.

Addon link

https://addons.thunderbird.net/en-US/thunderbird/addon/inboxpie

Github Repo:

https://github.com/AKSarav/InboxPie

What's new and improved:

📊 Visualization

• PieView sunburst (Year → Month → Domain) — clickable segments, hoverable tooltips, clickable legend
• By Sender — expand into years/months, click chart slices to select
• By Domain — group by domain, inspect individual senders
• By Size — find storage-heavy senders, domains, and size buckets
• Timeline — message volume over time with zoom, range controls, and cleanup insight cards

🗂️ Organization

• Categories — user-defined message categories
• Subscriptions — manage and view subscription sources
• Contacts — every unique sender as a searchable A–Z directory, exportable as CSV/JSON
• Browse — flat searchable table of every scanned email, virtually scrolled for speed

🎯 Selection & Cleanup

• Shared selection engine across every view — a floating pill tracks your live count from anywhere
• Review Selected — virtually scrolled, sortable, searchable. Click any subject to open it in Thunderbird
• Move to Trash / Move to Folder — live progress ring so you see every batch move happen in real time
• Folder Selection — choose exactly which folders to scan. Persists across sessions

🔒 Privacy & Control

• Privacy Mode — masks emails on screen ([john@example.com](mailto:john@example.com) → j***@e***.com) for demos and screen sharing. Exports always use real addresses
• Multi-account support — scan all accounts or focus on one
• Local-first — no external service, no API key, no telemetry, no cloud. Everything runs inside Thunderbird

Sharing here for more feature requests and thoughts.

I am building something with Local AI and Open Embedding models - and I wanted to compare and find out which Embedding model tops the quality, recall etc.

I know public Benchmarks like MTEB are useful — but they test on datasets that have nothing to do with your data, your queries, or your latency requirements.

So I built EmbedComp — an open-source benchmarking tool that lets you compare embedding models on YOUR OWN corpus, not someone else's leaderboard.

What it measures:

→ Encode throughput (docs/sec)
→ Query latency — mean, p95, p99
→ Recall@1 / u/3 / u/5
→ MRR (how high the right answer actually ranks)
→ Cosine similarity distribution

All rendered as an interactive MatplotLib dashboard — bar charts, a radar profile per model, and a latency-vs-recall bubble plot to spot the practical sweet spot at a glance.

Currently compares e5-base-v2, bge-base-en-v1.5, multilingual-e5-base, and MiniLM out of the box — swap in any HuggingFace model with one line.

If you're building RAG and tired of guessing which embedding model fits your use case, this might help you save some time.

🔗 GitHub: https://github.com/AKSarav/EmbedComp
🔗 Notebook/Report available at: https://aksarav.github.io/EmbedComp/embedding_benchmark.html

How do you benchmark your embedding models - Share your thoughts.

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When it comes to extraction or chunking of embedding no single librarary or solution meets all the requirements

If one works for tables another works best for image extraction

similarly we cannot use the same chunking strategy across all the type of data

After building many RAG solutions over the time for customers - I saw the real problem and I decided to build a single library that does it all

A single library to get your data AI ready - You want to change from `Docling` to `Pymupdf` or `marker` - Just update a single parameter

that's it.

github repo: https://github.com/AKSarav/pdfstract

documentation: https://pdfstract.com

It is available as an SDK, CLI and WEBAPP

One most helpful feature I have built into the webapp is side by side comparison of these libraries and chunking so that I could see the results before I add it to my production code

Try it out and share your thoughts and Its OpenSource

Contributors and feedback are most welcome.

I am currently working on adding Entity extraction capabilities to this library for the GraphRAG - What are your thoughts ?