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Sacred Fig Architecture (FIG): an adaptive, feedback-driven alternative to Hexagonal — thoughts?
https://www.reddit.com/r/programming/comments/1ovdngg/sacred_fig_architecture_fig_an_adaptive/

<!-- SC_OFF -->Hey everyone, I’ve been working on Sacred Fig Architecture (FIG) — an evolution of Hexagonal that treats a system like a living tree: Trunk = pure domain core Roots = infrastructure adapters Branches = UI/API surfaces Canopy = composition & feature gating Aerial Roots = built-in telemetry/feedback that adapts policies at runtime Key idea: keep the domain pure and testable, but make feedback a first-class layer so the system can adjust (e.g., throttle workers, change caching strategy) without piercing domain boundaries. The repo has a whitepaper, diagrams, and a minimal example to try the layering and contracts. Repo: github.com/sanjuoo7live/sacred-fig-architecture (http://github.com/sanjuoo7live/sacred-fig-architecture) What I’d love feedback on: Does the Aerial Roots layer (feedback → canopy policy) feel like a clean way to add adaptation without contaminating the domain? Are the channel contracts (typed boundaries) enough to keep Branches/Roots from drifting into Trunk concerns? Would you adopt this as an architectural model/pattern alongside Hexagonal/Clean, or is it overkill unless you need runtime policy adaptation? Anything obvious missing in the minimal example or the guardrail docs (invariants/promotion policy)? Curious where this breaks, and where it shines. Tear it apart! 🌳 <!-- SC_ON --> submitted by /u/Resident-Escape-7959 (https://www.reddit.com/user/Resident-Escape-7959)
[link] (http://github.com/sanjuoo7live/sacred-fig-architecture) [comments] (https://www.reddit.com/r/programming/comments/1ovdngg/sacred_fig_architecture_fig_an_adaptive/)
Day 15: Gradients and Gradient Descent
https://www.reddit.com/r/programming/comments/1ovjarm/day_15_gradients_and_gradient_descent/

<!-- SC_OFF -->What We’ll Build Today Implement a basic gradient descent algorithm from scratch Train a simple AI model to predict house prices using gradient descent Visualize how AI systems “learn” by following gradients downhill Why This Matters: The Secret Behind Every AI System <!-- SC_ON --> submitted by /u/Extra_Ear_10 (https://www.reddit.com/user/Extra_Ear_10)
[link] (https://aieworks.substack.com/p/day-15-gradients-and-gradient-descent) [comments] (https://www.reddit.com/r/programming/comments/1ovjarm/day_15_gradients_and_gradient_descent/)
LZAV 5.0: Improved compression ratio across a wide range of data types, at similar performance. Improved compression ratio by up to 5% for data smaller than 256 KiB. Fast Data Compression Algorithm (header-only C/C++).
https://www.reddit.com/r/programming/comments/1ovyqle/lzav_50_improved_compression_ratio_across_a_wide/

submitted by /u/avaneev (https://www.reddit.com/user/avaneev)
[link] (https://github.com/avaneev/lzav) [comments] (https://www.reddit.com/r/programming/comments/1ovyqle/lzav_50_improved_compression_ratio_across_a_wide/)
Raft Consensus in 2,000 words
https://www.reddit.com/r/programming/comments/1ow0czt/raft_consensus_in_2000_words/

<!-- SC_OFF -->Very accessible article about the Raft Consensus Algorithm - which solves the problem of choosing the leader in a distributed system environment. It's used in many popular tools and libraries, such as Etcd (database behind Kubernetes state), MongoDB or Apache Kafka. So it's definitely worth wrapping one's head around it; and as for a complex problem of this nature it's surprisingly straightforward and the linked article does a great job at explaining it in detail. <!-- SC_ON --> submitted by /u/BinaryIgor (https://www.reddit.com/user/BinaryIgor)
[link] (https://news.alvaroduran.com/p/raft-consensus-in-2000-words) [comments] (https://www.reddit.com/r/programming/comments/1ow0czt/raft_consensus_in_2000_words/)
Day 13: Implement TLS Encryption for Secure Log Transmission
https://www.reddit.com/r/programming/comments/1ow0nhs/day_13_implement_tls_encryption_for_secure_log/

<!-- SC_OFF --> Mutual TLS (mTLS) authentication between all distributed services Certificate management infrastructure with automated rotation Encrypted Kafka message streams with broker authentication TLS-secured REST endpoints with client certificate validation Performance benchmarking to quantify encryption overhead Resources : https://github.com/sysdr/sdc-java/tree/main/day13 https://sdcourse.substack.com/p/day-13-implement-tls-encryption-for <!-- SC_ON --> submitted by /u/Safe_Trick8865 (https://www.reddit.com/user/Safe_Trick8865)
[link] (https://sdcourse.substack.com/p/day-13-implement-tls-encryption-for) [comments] (https://www.reddit.com/r/programming/comments/1ow0nhs/day_13_implement_tls_encryption_for_secure_log/)
Pulse 1.0.4: deterministic concurrency, CLI tools and full templates
https://www.reddit.com/r/programming/comments/1owss92/pulse_104_deterministic_concurrency_cli_tools_and/

<!-- SC_OFF -->Hi everyone, I have been working on a small language called Pulse, a language that compiles to JavaScript but runs on its own deterministic runtime. If you like the idea of deterministic scheduling, channels and select inspired by Go, reactive signals, structured concurrency, and full JS ecosystem compatibility, you might find this interesting. What is Pulse Pulse is a small language with: deterministic cooperative scheduler CSP style channels and select signals, computed values and effects a full compiler pipeline: lexer, parser and codegen ES module output compatible with Node, Vite, Next, React, Vue Same inputs always produce the same async behavior. What is new in version 1.0.4 Version 1.0.4 focuses on real usability: stable CLI: pulse and pulselang commands create app tool: npx create-pulselang-app my-app full templates: React, Next and Vue templates now build correctly deterministic runtime verified again with fuzz and soak tests documentation and examples fully corrected ready for real world experiments Small example import { signal, effect } from 'pulselang/runtime/reactivity' import { channel, select, sleep } from 'pulselang/runtime/async' fn main() { const [count, setCount] = signal(0) const ch = channel() effect(() => { print('count is', count()) }) spawn async { for (let i = 1; i <= 3; i++) { await ch.send(i) setCount(count() + 1) } ch.close() } spawn async { for await (let value of ch) { print('received', value) } } } The scheduler runs this with the same execution order every time. How to try it Install: npm install pulselang Run: pulse run file.pulse Create a template app (React + Vite + Tailwind): npx create-pulselang-app my-app cd my-app npm run dev Links Docs and playground: https://osvfelices.github.io/pulse Source code: https://github.com/osvfelices/pulse If you try it and manage to break the scheduler, the channels or the reactivity system, I would love to hear about it. <!-- SC_ON --> submitted by /u/coloresmusic (https://www.reddit.com/user/coloresmusic)
[link] (https://osvfelices.github.io/pulse) [comments] (https://www.reddit.com/r/programming/comments/1owss92/pulse_104_deterministic_concurrency_cli_tools_and/)