Som VS timeline

> Smalltalkish

Have a look at the SOM dialect which is successfully used in education: http://som-st.github.io/

Here is an implementation in C++ which runs on LuaJIT: https://github.com/rochus-keller/Som/

> unfortunately out of print book Smalltalk 80: the language and its implementation is commonly recommended

I assume you know this link: http://stephane.ducasse.free.fr/FreeBooks/BlueBook/Bluebook....

Here is an implementation in C++ and Lua: https://github.com/rochus-keller/Smalltalk

- https://github.com/rochus-keller/Smalltalk/ Parser, code model, interpreter and navigable browser for the original Xerox Smalltalk-80 v2 sources and virtual image file

- https://github.com/rochus-keller/Som/ Parser, code model, navigable browser and VM for the SOM Smalltalk dialect

- https://github.com/rochus-keller/Simula A Simula 67 parser written in C++ and Qt

> do you regret those endeavours?

No, not in any way; the projects were very entertaining and gave me interesting insights.

If your DSL is statically typed then I recommend that you have a look at the Mono CLR; it's compatible with the ECMA-335 standard and the IR (CIL) is well documented, even with secondary literature.

If your DSL is dynamically typed I recommend LuaJIT; the bytecode is lean and documented (not as good as CIL though). LuaJIT also works well with statically typed languages, but Mono is faster in the latter case. Even if it was originally built for Lua any compiler can generate LuaJIT bytecode.

Both approaches are lean (Mono about 8 MB, LuaJIT about 1 MB), general purpose, available on many platforms and work well (see e.g. https://github.com/rochus-keller/Oberon/ and https://github.com/rochus-keller/Som/).

At the latest when you run a benchmark suite like Are-we-fast-yet; here are some measurment results: http://software.rochus-keller.info/are-we-fast-yet_crystal_lua_node_som_pharo_i386_results_2020-12-29.pdf. See also https://github.com/rochus-keller/Som/ and https://github.com/rochus-keller/Smalltalk.

LuaJIT is well suited as a backend/runtime environment for custom languages; I did it several times (see e.g. https://github.com/rochus-keller/Smalltalk, https://github.com/rochus-keller/Som/, https://github.com/rochus-keller/Oberon/). I also implemented a bit of infrastructure to ease the reuse: https://github.com/rochus-keller/LjTools. LuaJIT has some limitations though; if you require closures you have to know that the corresponding LuaJIT FNEW bytecode is not yet supported by the JIT, i.e. switches to the interpreter; as a work-around I implemented my own closures; LuaJIT also doesn't support multi-threading, but co-routines; and there is no debugger, and the infrastructure to implement one has limitations (i.e. performance is low when running to breakpoints). For most of my projects this was no issue. Recently I switched to CIL/Mono for my Oberon+ implementation which was a good move. But still I consider LuaJIT a good choice if you can cope with the mentioned limitations. The major advantage of LuaJIT is the small footprint and impressive performance for dynamic languages.

One option is to leverage someone else's JIT: you could, for example, rewrite the interpreter to transpile to Lua source, which is then run in LuaJIT. There's a Smalltalk dialect which does this successfully; the Lua version runs in 1/12th the time of the C interpreted version. https://github.com/rochus-keller/Som You can use LuaJIT's FFI to call back into the Stunt server, or else just rewrite it completely in Lua --- large parts of the Stunt server will just go away in a native Lua implementation (e.g. the object database is just a table). Javascript would be another candidate for this.

This is a completely different kind of measurement; unfortunately this is not clear enough from my Readme. I wanted to find out, how well my naive Bluebook interpreter performs on LuaJIT (using my virtual meta tracing approach) compared to Cog, which is a dedicatd Smalltalk VM optimized with whatever genious approaches over two decades (or even longer considering the long experience record by Elliot). This experiment continues in https://github.com/rochus-keller/Som, because I didn't want to modify the original Smalltalk image. I found that my naive LuaJIT based approach is about factor seven behind the highly optimized Cog/Spur, and further improvements would require similar optimization tricks as in the latter.

My timeline thing. It gathers all my crap and puts in onto a timeline. It's a more fine-grained version of scrolling to a specific date on my photo stream.

https://github.com/nicbou/timeline

It serves no purpose, but somehow it attracted one contributor.

It's pointless on purpose. It's the thing I work on when I want to forget about work, and build purely for myself.

Google Drive as a first line of defence. It's been solid for a really long time.

I also run hourly rsync backups to my home server, and propagate them to a Hetzner file storage server. This is done by my timeline thing [0]. The timeline thing backs up files from multiple devices, but also geolocation, social media posts, and other data I consider valuable. It's extensible, so I can add new inputs/outputs as needed.

Whatever your backup strategy is, consider the following threats:

- Your files are held hostage by ransomware, and the damage spreads to your backup

- Your house is destroyed by fire

- You lose your 2FA device

- You are locked out of your Google/Apple/Microsoft account

- You are incapacitated, and someone needs to take over

I have 4 of those factors covered. I am working on the last point.

[0] https://github.com/nicbou/timeline

My own timeline thing.

It hosts all of my data plus my personal diary. I update it at least once a day. My photos, backups and geolocation are automatically uploaded to it.

https://github.com/nicbou/timeline

My home server gets a lot of use too. It's mostly my own code, plus Transmission.

https://github.com/nicbou/homeserver

I also have a few lines of code that take my browser's search queries and routes them according to keywords. Browsers do this natively now, but old habits die hard. Every search query goes through it.

I added RSS to my websites, because my timeline thing (https://github.com/nicbou/timeline) uses them to retrieve posts from my websites.

However, I see the death of RSS as the symptom of a larger problem: when platforms get big enough, they restrict access to their data. RSS feeds disappear, but so do other machine-readable endpoints. If it wasn't for GDPR, there would be no way to export that data. GDPR gave us clunky one-time exports, but even those are often incomplete.

The industry has a strong incentive to kill RSS, since the readers can strip the valuable bits (content or data) from the business bits (analytics, monetisation). RSS users are hard to count or monetise.

This is a battle worth fighting, but it's not one you should expect to win.

https://github.com/nicbou/timeline

It regroups my personal data, and displays it on a timeline. Sort of like if Google Photos also included reddit posts, personal journal entries, text messages and other slices of life.

I do it both as a way to back up files and photos, and as a way to keep an enhanced journal.