FI

FISE – A rule-based, keyless semantic envelope for web/API data

Hacker News

FISE – A rule-based, keyless semantic envelope for web/API data

Hi HN, I've been building a small open-source project called FISE, a rule-based "semantic envelope" for web/API data. It's not cryptography and it doesn't try to replace TLS/AES. The goal is to raise the cost of scraping and reverse-engineering client-visible data while keeping the hot path simple and fast (linear-time, parallelizable, works for JSON and media segments). Key ideas: - Keyless by design: no long-lived client-side decrypt keys. - Rules-as-code: offsets, length encoding, salt/meta layout, and optional transform are defined as per-app/per-session rules. - No protocol-level universal decoder: each deployment (and even each session/time bucket) can have its own rule family. - Temporal & distribution polymorphism: rotate rules per session/route/time so any decoded pipeline tends to be short-lived and non-reusable. - Simple local ops → emergent complexity: the core ops are just linear byte transforms, but the rule space and rotation make envelopes hard to generalize at scale. - Works for JSON and media (video segments) with framed/chunked mode, block-local decode, and parallel workers. Threat model: - In scope: automated scraping, bulk API harvesting, cheap cloning of curated datasets. - Out of scope: strong secrecy for secrets/PII (use TLS/AES/etc.), full client compromise, nation-state adversaries. Engineering whitepaper (v1.0): - https://github.com/anbkit/fise/blob/main/docs/WHITEPAPER.md Code: - https://github.com/anbkit/fise Demo: - https://demo.fise.dev/demo I'm a full-stack developer, not a cryptographer. FISE came out of working on real projects where API JSON data was exposed on the client side and could be easily scraped. I built it as a simple, rule-based layer to raise scraping cost, and I'm sharing it so others can review, critique, and improve it. I'd love feedback on: - The security model & threat boundaries (what did I miss?), - The "rules-as-code" design and the idea of temporal/distribution polymorphism, - Practical deployment concerns (CDN/normalization, mobile/TV/edge), - Any obvious pitfalls or prior art I should explicitly reference. Thanks for taking a look.

Share card

Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, coding, code · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

I
I Made a Web API for Textual Data65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I Made a Web API for Textual Data

Hacker News27
On
One hostname to rule them all37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

One hostname to rule them all

Hacker News111
On
One binary to rule them all45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

One binary to rule them all

Hacker News3
On
One makefile to rule them all37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

One makefile to rule them all

Hacker News57
Ta
Tagging for Semantic Web made easy68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tagging for Semantic Web made easy

Hacker News2
Se
Semantic programmatic search for computable data60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Semantic programmatic search for computable data

Hacker News24
Sc
SchemaVer for semantic versioning of schemas64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SchemaVer for semantic versioning of schemas

Hacker News1
SH
SHOW HN: Building an app to rule all web-based SaaS, need feedbacks from HN42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SHOW HN: Building an app to rule all web-based SaaS, need feedbacks from HN

Hacker News6
NKTg AI
NKTg AI22%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Physics-based semantic AI for absolute data privacy

Indie Hackerscommitment-full-time
We
Web Based Multipler Roguelike63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Web Based Multipler Roguelike

Hacker News3