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An Occam to Go transpiler (LLM-generated)

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An Occam to Go transpiler (LLM-generated)

Occam and golang share a common ancestry for their approach to concurrency, in CSP. So I've long wondered whether Occam could be successfully translated into golang. I began this project: https://github.com/codeassociates/occam2go as an experiment to see if LLM coding could help test that idea. Also, the project seemed like a worthy challenge in light of comments I'd read around the recent Anthropic "Claude-written C compiler" announcement. Those comments said that an LLM can write a C compiler easily because there are many C compilers in its training data. Since there's little to no Occam in today's LLM training data sets (GitHub doesn't even recognize it as a distinct language) this project should be impossible for an LLM, right? (Spoiler: wrong). Every line of code in the repo was LLM-generated. The transpiler currently builds and runs its test suite Occam programs, a number of old Occam demo/example programs and the Conway game of life Occam program from the 1988 book "Programming in Occam2". I'd be interested to hear if it can run other Occam code that may be out there somewhere. Almost all of the Occam2 language should be supported, albeit with some limitations such as PRI PAR not actually delivering priority semantics at runtime. There's also a short article that links to the more interesting Claude sessions recorded during the project: https://codeassociates.github.io/conversations-with-claude/c...

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, coding, code · Missing: mac, agents, macos
87%87% 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 · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
35%35% 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
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
24%24% 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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