

prologic@JamessMacStudio
Sun May 25 21:44:41
~/tmp/neurog
(main) 130
$ go build ./cmd/ttt/... && ./ttt
Generation 27 | Fitness: 0.486111 | Nodes: 44 | Conns: 82
... experimenting with building and training a tic-tac-toe game, which evolves a. neural net that learn to paly the game against the best evolved champions 😅
(qualquer marca excepto Xiaomi, nessa já não me apanham)
(qualquer marca excepto Xiaomi, nessa já não me apanham)
https://www.youtube.com/watch?v=E7LYCERDnX4
https://www.youtube.com/watch?v=E7LYCERDnX4
- Minimal syntax & concepts → low learning curve
- Compiled speed → high throughput
- Built-in CSP concurrency → scalable by default
See Rob Pyke's presentation on Expressiveness of Go
* Go:
25
keywords ([Stack Overflow][1]); CSP-style concurrency (goroutines & channels)* Python 2:
30
keywords ([TutorialsPoint][2]); GIL-bound threads & multiprocessing ([Wikipedia][3])* Python 3:
35
keywords ([Initial Commit][4]); GIL-bound threads, asyncio
& multiprocessing ([Wikipedia][3], [DEV Community][5])* Java:
50
keywords ([Stack Overflow][1]); threads + java.util.concurrent
([Wikipedia][6])* C++:
82
keywords ([Stack Overflow][1]); std::thread
, atomics & futures ([en.cppreference.com][7])* JavaScript:
38
keywords ([Stack Overflow][1]); single-threaded event loop & async/await
, Web Workers ([Wikipedia][8])* Ruby:
42
keywords ([Stack Overflow][1]); GIL-bound threads (MRI), fibers & processes ([Wikipedia][3])[1]: https://stackoverflow.com/questions/4980766/reserved-keywords-count-by-programming-language?utm_source=chatgpt.com "Reserved keywords count by programming language?"
[2]: https://www.tutorialspoint.com/What-are-Reserved-Keywords-in-Python?utm_source=chatgpt.com "Reserved Keywords in Python - Online Tutorials Library"
[3]: https://en.wikipedia.org/wiki/Global_interpreter_lock?utm_source=chatgpt.com "Global interpreter lock"
[4]: https://initialcommit.com/blog/python-reserved-words?utm_source=chatgpt.com "Python Reserved Keywords (Full List) - Initial Commit"
[5]: https://dev.to/sreeni5018/understanding-pythons-gil-and-enhancing-concurrency-with-multithreading-multiprocessing-and-5g1e?utm_source=chatgpt.com "Understanding Python’s GIL and Enhancing Concurrency with ..."
[6]: https://en.wikipedia.org/wiki/Java_concurrency?utm_source=chatgpt.com "Java concurrency - Wikipedia"
[7]: https://en.cppreference.com/w/cpp/thread?utm_source=chatgpt.com "Concurrency support library (since C++11) - cppreference.com"
[8]: https://en.wikipedia.org/wiki/JavaScript?utm_source=chatgpt.com "JavaScript"*
- Simple, minimal syntax—master the core in hours, not months.
- CSP-style concurrency (goroutines & channels)—safe, scalable parallelism.
- Blazing-fast compiler & single-binary deploys—zero runtime dependencies.
- Rich stdlib & built-in tooling (gofmt, go test, modules).
- No heavy frameworks or hidden magic—unlike Java/C++/Python overhead.
:=
and it's just infinitely harder for me to parse and infer meaning from lol. it's such a me problem
:=
and it's just infinitely harder for me to parse and infer meaning from lol. it's such a me problem

$ go build ./cmd/xor/... && ./xor
Generation 95 | Fitness: 0.999964 | Nodes: 9 | Conns: 19
Target reached!
Best network performance:
[0 0] → got=0 exp=0 (raw=0.000) ✅
[0 1] → got=1 exp=1 (raw=0.990) ✅
[1 0] → got=1 exp=1 (raw=0.716) ✅
[1 1] → got=0 exp=0 (raw=0.045) ✅
Overall accuracy: 100.0%
Wrote best.dot – render with `dot -Tpng best.dot -o best.png`


Twtxt not sloe enough for you? 🤣

fit 1 $ spin (saw 0.1 * sign fxy) $ rect 0 1 - rect 0 0.99 >> add;
#punctual #livecoding #creativecoding #videoart

fit 1 $ spin (saw 0.1 * sign fxy) $ rect 0 1 - rect 0 0.99 >> add;
#punctual #livecoding #creativecoding #videoart

fit 1 $ spin (saw 0.1 * sign fxy) $ rect 0 1 - rect 0 0.99 >> add;
#punctual #livecoding #creativecoding #videoart

fit 1 $ spin (saw 0.1 * sign fxy) $ rect 0 1 - rect 0 0.99 >> add;
#punctual #livecoding #creativecoding #videoart
"A coalition of determined open-source software (OSS) advocates and a handful of technology experts working in the European Commission set out in 2004 to end Microsoft's monopoly. They almost succeeded. This article reveals how they managed to change the EU's software policies, made Microsoft lobbyists work overtime - and in the end, and despite their best efforts, could not withstand the power of proprietary companies’ lobbying campaigns.
Drawing on the Multiple Streams Framework, the article explains the European Commission’s decision to promote OSS and open standards in 2004, and its puzzling decision to reverse course just a few years later, in 2010, despite its unchanged rhetoric about the benefits of openness. The analysis reveals three key factors that drove the changes in the EU’s policies.
In 2004, OSS advocates managed to frame the EU’s dependency on proprietary software as a problem – and the promotion of OSS and open standards as the solution.
In 2010, #Microsoft and other proprietary companies used their existing connections in Brussels to sow doubt about the maturity and cost of #OSS among #EU policymakers."
25 years later we're where we started.
#OpenSource #EIF