SF

SFOR – minimal, no-backtracking, typed data format (experimental)

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SFOR – minimal, no-backtracking, typed data format (experimental)

I have been working on SFOR (Streamable Flat Object Representation) — a structured text format I built after finding that existing formats did not quite fit my needs.. - *Streamable:* Can be parsed as it arrives, without needing the entire payload in memory. - *No-backtracking:* The parser never has to "rewind" to re-interpret earlier data. - *Type-explicit:* Types are clearly defined in the stream, avoiding ambiguity. - *Minimal overhead:* Compact representation without sacrificing readability in its intermediate form. The goal is to handle cases where formats like JSON or YAML work, but a streamable, predictable, and low-memory alternative is better suited — especially for constrained devices, large datasets, or real-time processing. I would appreciate your thoughts on: - Potential edge cases I haven not considered. - Comparisons to similar formats you have used. - Whether the "no required backtracking" claim holds up in your experience with other formats. *Repo:* - [SFOR Repo]( https://github.com/brucekaushik/sfor )

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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