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ScentHash
A digital representation layer for scent
Images, audio, and video all have digital formats. Scent doesn't. ScentHash is an experimental representation layer that encodes any scent into a structured base-note recipe, a 40-D fingerprint, and a SHA-256 identity — using a live, deterministic AI encoder. Try it now.s.
Hi Product Hunt 👋
I'm Abdallah Albliwi, building ScentHash as a solo maker from jordan.
The idea started from a simple observation: everything digital has a format — images (JPEG), audio (MP3), video (MP4), documents (PDF). But scent has none. There's no common way to store, compare, or exchange a smell as data.
ScentHash is my attempt at that missing layer. You type a scent in words, and a live, deterministic AI encoder (Llama-3.3-70B via Groq) maps it into:
• a base-note recipe (from a 40-note experimental palette)
• a 40-dimensional fingerprint
• a SHA-256 identity + version
Same words → same result, every time. You can also compare scents by similarity, and there's an experimental "lab" view that visualizes the whole pipeline.
I want to be upfront (it matters to me): this is v0.1 and honest about its limits. The 40-note model is an experimental representation — not a claim that every smell reduces to 40 notes. The sensor/lab data is clearly labeled as simulated (no physical scent hardware yet). What IS real: the AI encoder, the fingerprint, the hashing, and the similarity engine — all live today.
I'd genuinely value your honest feedback — on the concept, the encoder quality, or where you'd take a "digital layer for scent" next.
Try it: https://scenthash.com
Thanks for taking a look 🙏
About ScentHash on Product Hunt
“A digital representation layer for scent”
ScentHash was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #17 on the daily leaderboard. Images, audio, and video all have digital formats. Scent doesn't. ScentHash is an experimental representation layer that encodes any scent into a structured base-note recipe, a 40-D fingerprint, and a SHA-256 identity — using a live, deterministic AI encoder. Try it now.s.
On the analytics side, ScentHash competes within Design Tools, Developer Tools and Artificial Intelligence — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how ScentHash performed against the three products that launched closest to it on the same day.
Who hunted ScentHash?
ScentHash was hunted by Abdallah Albliwi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ScentHash including community comment highlights and product details, visit the product overview.
Abdallah Albliwi