2025-12-27 01:56:42
The 2025 CoinGlass crypto derivatives annual report shows that Bitunix exchange is not only participating in the market but actively shaping it. Trading volume reflects the frequency with which traders engage with the platform, while open interest indicates the amount of exposure being held in ongoing contracts. Being ranked in the top ten for both measures demonstrates that Bitunix’s growth is steady, sustainable, and built on real market activity rather than short-term spikes.
2025-12-27 01:00:16
There is no silence quite as loud as the Slack notification channel after a failed deployment on a Friday afternoon.
\ You know the scene. You pushed the code three hours ago. The logic is sound, the tests passed locally, and the PR was approved. Yet, you are still staring at a spinning circle—or worse, a red "X"—in your GitHub Actions dashboard.
\ Is it a missing secret? A mismatched Node version? A permission error in the AWS role?
\ We have entered an era where "shipping code" often involves more time wrestling with YAML indentation and container permissions than actually writing the software. We aren't just developers anymore; we are part-time plumbers, tasked with maintaining an increasingly complex web of pipes that connect our code to the cloud.
\ The promise of DevOps was to automate the pain away. The reality? We just automated the creation of new, more confusing pain.
\ It is time to stop hand-crafting these digital pipelines like they are artisanal furniture. It is time to treat CI/CD configuration as what it effectively is: infrastructure logic that should be architected, not guessed.
Modern CI/CD isn't just "build and deploy" anymore. It's a gauntlet.
\ To ship a standard microservice today, you need to handle:
\ Expecting a full-stack developer to memorize the syntax for every caching strategy in GitHub Actions or every security flag in GitLab CI is not just unrealistic; it's inefficient. It leads to "Copy-Paste DevOps," where we drag the same mediocre, insecure pipeline configuration from project to project, inheriting its flaws like a genetic defect.
\ We need a better way. We need an architect who knows every flag, every security best practice, and every optimization trick available on demand.
I stopped trying to memorize the intricacies of AWS EKS authentication and started forcing my AI tools to act as the Senior DevOps Architect I wish I had on speed dial.
\ I created a CI/CD Pipeline System Prompt designed to turn generic LLMs into rigorous automation experts. It doesn't just "make a pipeline"; it interviews you about your stack, your constraints, and your goals, then designs a pipeline that is secure, fast, and resilient by default.
\
Copy this prompt. Use it before you write your next .yaml file.
# Role Definition
You are a Senior DevOps Architect and CI/CD Specialist with 10+ years of experience designing and implementing enterprise-grade automation pipelines. You have deep expertise in:
- Pipeline orchestration tools (GitHub Actions, GitLab CI, Jenkins, Azure DevOps, CircleCI)
- Container orchestration (Docker, Kubernetes, Helm)
- Infrastructure as Code (Terraform, Pulumi, CloudFormation)
- Security scanning and compliance automation (SAST, DAST, SCA)
- Multi-environment deployment strategies (Blue-Green, Canary, Rolling)
- Observability and monitoring integration
# Task Description
Design and optimize a CI/CD pipeline based on the provided project requirements. Your goal is to create a robust, secure, and efficient automation workflow that accelerates software delivery while maintaining quality and reliability.
Please analyze the following project details and create a comprehensive CI/CD solution:
**Input Information**:
- **Project Type**: [e.g., microservices, monolith, serverless, mobile app]
- **Tech Stack**: [e.g., Node.js, Python, Java, Go, React]
- **Deployment Target**: [e.g., AWS EKS, GCP GKE, Azure AKS, bare metal]
- **Team Size**: [number of developers]
- **Current Pain Points**: [manual deployments, slow builds, lack of testing, etc.]
- **Security Requirements**: [compliance standards, security scanning needs]
- **Existing Tools**: [current CI/CD tools, if any]
# Output Requirements
## 1. Content Structure
- **Pipeline Architecture**: Visual representation and detailed explanation of the pipeline stages
- **Stage Configuration**: Specific configuration for each pipeline stage
- **Security Integration**: Security scanning and compliance automation
- **Environment Strategy**: Multi-environment deployment approach
- **Monitoring & Alerting**: Observability integration recommendations
## 2. Quality Standards
- **Reliability**: Pipeline should have <1% failure rate for non-code-related issues
- **Speed**: Build and deploy should complete within acceptable time limits
- **Security**: All security gates must pass before production deployment
- **Scalability**: Design should accommodate team growth and increased deployment frequency
- **Maintainability**: Configuration should be modular and well-documented
## 3. Format Requirements
- Provide pipeline configuration in YAML format (GitHub Actions, GitLab CI, or requested tool)
- Include inline comments explaining each step
- Provide a pipeline diagram using Mermaid or ASCII art
- List all required secrets and environment variables
- Include rollback procedures
## 4. Style Constraints
- **Language Style**: Technical but accessible, avoiding unnecessary jargon
- **Expression**: Direct and actionable with clear reasoning
- **Depth**: Deep technical detail with practical implementation guidance
# Quality Checklist
Before delivering, verify:
- [ ] Pipeline covers all stages: build, test, security scan, deploy, verify
- [ ] Secrets management is properly addressed
- [ ] Rollback strategy is clearly defined
- [ ] Pipeline is optimized for speed (parallel jobs, caching)
- [ ] Security scanning is integrated at appropriate stages
- [ ] Environment-specific configurations are separated
- [ ] Monitoring and alerting hooks are included
- [ ] Documentation for maintenance and troubleshooting is provided
# Important Notes
- Always use locked/pinned versions for actions and dependencies
- Never expose secrets in logs or artifacts
- Implement proper branch protection and approval workflows
- Consider cost implications for cloud-based runners
- Design for idempotency - pipelines should be safely re-runnable
# Output Format
Provide the complete CI/CD solution in the following structure:
1. Executive Summary (2-3 sentences)
2. Pipeline Architecture Diagram
3. Complete Pipeline Configuration (YAML)
4. Stage-by-Stage Explanation
5. Security Considerations
6. Environment Variables and Secrets List
7. Rollback Procedures
8. Optimization Recommendations
9. Maintenance Guidelines
This approach works because it shifts the focus from syntax to strategy.
Notice the checklist item regarding optimization. A junior engineer (or a basic AI query) might write a linear pipeline: install -> test -> build -> deploy.
\
This architect knows better. It will look for opportunities to run independent jobs in parallel. It will implement aggressive caching for node_modules or Docker layers. It treats time as a resource to be conserved, not just a duration to endure.
The prompt explicitly mandates Security Integration. It forces the inclusion of tools like Snyk for dependencies or Trivy for container scanning inside the pipeline. It ensures that security isn't something you "check later"—it's a gate that stops bad code from ever leaving the build environment.
Most pipelines fail at Rollback Procedures. We assume success. This prompt assumes failure. It demands a defined rollback strategy. What happens if the deployment fails? How do we revert? By forcing these questions upfront, you build a system that is resilient to the chaos of the real world.
The goal of your job is not to be a Master of YAML. It is to deliver value to users. Every hour you spend debugging a pipeline syntax error is an hour you aren't improving your product.
\ Let the AI handle the plumbing. You focus on the water.
\ By using a structured system prompt, you ensure that your automation infrastructure is built on a foundation of best practices, not just whatever StackOverflow snippet worked for someone else three years ago.
\ Escape the loop. Architect your escape.
2025-12-27 01:00:08
Hey friends,
\ As the final days of 2025 blink out on our monitors, there’s a natural pull to reflect. For us in software, a year isn’t just a calendar flip. It’s several product cycles, a handful of migrations, a parade of new frameworks whispered about, tried, and sometimes abandoned. It moves fast.
\ I’ve been sitting here, coffee in hand, scrolling through notebook scribbles, not to audit myself, but to listen. To what felt good, what felt like friction, and where the quiet sense of satisfaction actually came from. I thought I’d share my process, not as a blueprint, but as one perspective in our shared journey. Maybe some of these questions will resonate as you think about your own path into 2026.
Before we charge ahead, let’s look back without judgment. The goal isn’t to tally wins and losses, but to notice patterns.
\ The Energy Audit: Which tasks or projects consistently left you feeling energized, even when tired? Was it that deep dive into a gnarly performance issue, the mentorship you casually provided, or the UI polish that finally felt right? Conversely, what routinely drained you? Was it the context-switching, a particular type of meeting, or the weight of a legacy system? Our energy is a precious resource. What were the highest and lowest consumers? \n
The Learning That Stuck: Forget the buzzword bingo list. What did you genuinely learn this year that changed how you work? Maybe it wasn’t a new language, but a deeper understanding of your system’s observability, or a better way to write a test description so it fails clearly. What small piece of knowledge feels solid under your feet now?
\ The “Why” Moments: Recall a moment of significant frustration or a moment of pure flow. What was happening around you? The "why" behind these peaks and valleys often points to our unspoken needs for clarity, autonomy, collaboration, or deep focus.
Armed with those gentle observations, 2026 becomes less about a rigid “new you” and more about intentional shifts. Not resolutions, but directions.
\ From “Learn More” to “Learn Specific, Learn Deeply.” The tech ocean is endless. What’s one current that would genuinely help you sail where you want to go? It could be deepening expertise in your stack’s core, not just its edges, or learning enough about the business domain to better anticipate needs. Or maybe it’s a “soft” skill like facilitating a better brainstorming session or handling review processes.
\ Designing Your Time, Not Just Managing It. Based on your energy audit, can you subtly reshape your weeks? Could you advocate for a “focus block” on your calendar? Batch your code reviews? Even small adjustments to protect your focus or collaborative time can have a compounding effect on well-being and output.
\ The Connection Compass. Our work isn’t done in isolation. Who do you want to learn from? Who might benefit from your experience? Maybe 2026 is about seeking out one person for a monthly coffee chat, or contributing more deliberately to your team’s documentation or onboarding process. Strong networks are built on consistent, small contributions.
\ Prioritizing the “Foundation.” We maintain codebases, but often neglect our own foundation. What does maintenance look like for you? Is it setting stricter boundaries to avoid burnout? Is it finally automating that tedious deploy step? Is it dedicating time to pay down a tiny bit of personal tech debt? A stable base lets you build amazing things without toppling over.
In our quest to be better engineers, let’s not forget to be human beings who code. The most sustainable strategy is one that includes curiosity, rest, and the occasional unplugged walk. Our craft is a marathon of learning and adaptation, not a sprint to the latest trend.
\ Here’s to a 2026 where we build not just with better technology, but with better intention, kindness towards our past selves, and a clear-eyed view of what makes us truly effective and fulfilled.
\ I’d love to hear what you’re reflecting on. What’s one small insight you’re carrying into the new year?
\ Cheers 🥂,
A Fellow Engineer.
2025-12-27 00:08:52
Anonymous Instagram Story viewing isn’t about stalking—it’s about strategy. This article explores how viewing public Stories without leaving a digital footprint reshaped competitive research, reduced social pressure, and challenged Instagram’s engagement-first design. From tool breakdowns to ethical boundaries, it shows why silent observation can be both practical and responsible.
2025-12-27 00:02:01
How are you, hacker?
🪐 What’s happening in tech today, December 26, 2025?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, George Washington Won First US Victory for the Continental Army in 1776, Los Angeles Celebrated the First Kwanzaa in 1966, FDR Officially Established the Modern Thanksgiving Holiday in 1941, and we present you with these top quality stories. From Java’s Growing Graveyard: The Old APIs Being Buried—and What Replaced Them to The Most Dangerous Person on Your Team is Dave (And He Just Quit), let’s dive right in.
By @akiradoko [ 9 Min read ] The Java “tomb” is filling up. Here’s what’s being buried—and what you should use instead. Read More.
By @riedriftlens [ 5 Min read ] Before ethics becomes a policy, a guideline, or a line of code, it must first exist as a human capacity. Read More.
By @zbruceli [ 20 Min read ] Groq’s Deterministic Architecture is Rewriting the Physics of AI Inference. How Nvidia Learned to Stop Worrying and Acquired Groq Read More.
By @huizhudev [ 5 Min read ] Stop letting knowledge walk out the door. Use this system prompt to turn every commit into a well-documented masterpiece. Read More.
🧑💻 What happened in your world this week?
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We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
2025-12-26 23:00:09
For a while now, I’ve been experimenting with AI coding tools and there’s something fascinating happening when you combine Rust with agents such as Claude Code or OpenAI’s Codex: The experience is fundamentally different from working with Python or JavaScript - and I think it comes down to one simple fact: Rust’s compiler acts as an automatic expert reviewer for each edit the AI makes.
\
If it compiles, it probably works; that’s not just a Rust motto: it’s becoming the foundation for reliable AI-assisted development.
When you let Claude Code or Codex loose on a Python codebase, you essentially trust the AI to get things right on its own: Sure, you have linters and type hints (if you are lucky), but there is no strict enforcement: the AI can generate code that looks reasonable, passes your quick review, and then blows up in production because of some edge case nobody thought about.
\ With Rust, the compiler catches these issues before anything runs. Memory safety incidents? Caught. Data runs? Caught. Lifetime issues? You guessed it—caught in compiler time. This creates a remarkably tight feedback loop that AI coding tools can actually learn from in real time.
Here is what makes Rust special for AI coding: the compiler doesn’t just say “Error” and leave you guessing: It tells you exactly what went wrong, where it went wrong, and often suggests how to fix it; this is absolute gold for AI tools like Codex or Claude Code.
\ Let me show you what I mean: say the AI writes this code:
fn get_first_word(s: String) -> &str {
let bytes = s.as_bytes();
for (i, &item) in bytes.iter().enumerate() {
if item == b' ' {
return &s[0..i];
}
}
&s[..]
}
\ The Rust compiler doesn’t fail just with a cryptic message, but it gives you:
error[E0106]: missing lifetime specifier
--> src/main.rs:1:36
|
1 | fn get_first_word(s: String) -> &str {
| - ^ expected named lifetime parameter
|
= help: this function's return type contains a borrowed value,
but there is no value for it to be borrowed from
help: consider using the `'static` lifetime
|
1 | fn get_first_word(s: String) -> &'static str {
| ~~~~~~~~
\ Look at this. The compiler is literally explaining the ownership model to AI - it is saying to - “Hey, you’re trying to return a reference, but the thing you’re referencing will be dropped when this function ends - that’s not going to work.”
\ For an AI coding tool, this is structured, deterministic feedback. The error code E0106 is consistent; the location is found to the exact character; the explanation is clear; and there’s even a suggested fix (though in this case, the real fix is to change the function signature to borrow instead of taking ownership).
\ Here’s another example that constantly happens when AI tools write concurrent code:
use std::thread;
fn main() {
let data = vec![1, 2, 3];
let handle = thread::spawn(|| {
println!("{:?}", data);
});
handle.join().unwrap();
}
\ The compiler response:
error[E0373]: closure may outlive the current function, but it borrows `data`
--> src/main.rs:6:32
|
6 | let handle = thread::spawn(|| {
| ^^ may outlive borrowed value `data`
7 | println!("{:?}", data);
| ---- `data` is borrowed here
|
note: function requires argument type to outlive `'static`
--> src/main.rs:6:18
|
6 | let handle = thread::spawn(|| {
| ^^^^^^^^^^^^^
help: to force the closure to take ownership of `data`, use the `move` keyword
|
6 | let handle = thread::spawn(move || {
| ++++
\ The compiler literally tells the AI: “Add move here, Claude Code or Codex can parse it, apply the fix and move on - no guesswork, no hoping for the best, no Runtime - Data Races that crash your production system at 3 AM.
\ This is fundamentally different from what occurs in Python or JavaScript: When an AI produces buggy concurrent code in those languages, you might not even know there is a problem until you hit a race condition under specific load conditions; with Rust, the bug never makes it past the compiler.
I came across an interesting observation from Julian Schrittwieser at Anthropic, who put it perfectly:
Rust is great for Claude Code to work unsupervised on larger tasks. The combination of a powerful type system with strong security checks acts like an expert code reviewer, automatically rejecting incorrect edits and preventing bugs.
\ This matches our experience at Sayna, where we built our entire voice processing infrastructure in Rust. When Claude Code or any AI tool changes, the compiler immediately tells it what went wrong; there is no waiting for runtime errors, no debugging sessions to figure out why the audio stream randomly crashes; the errors are clear and actionable.
\ Here’s what a typical workflow looks like:
# AI generates code
cargo check
# Compiler output:
error[E0502]: cannot borrow `x` as mutable because it is also borrowed as immutable
--> src/main.rs:4:5
|
3 | let r1 = &x;
| -- immutable borrow occurs here
4 | let r2 = &mut x;
| ^^^^^^ mutable borrow occurs here
5 | println!("{}, {}", r1, r2);
| -- immutable borrow later used here
# AI sees this, understands the borrowing conflict, restructures the code
# AI makes changes
cargo check
# No errors, we're good
\ The beauty here is that every single error has a unique code (E0502 in this case). If you run rustc –explain E0502, you get a full explanation with examples. AI tools can use this to understand not only what went wrong but also why Rust’s ownership model prevents this pattern, because the compiler essentially teaches the AI as it codes.
\ The margin for error becomes extremely small when the compiler provides structured, deterministic feedback that the AI can parse and act on.
\ Compare this to what you get from a C++ compiler if something goes wrong with templates:
error: no matching function for call to 'std::vector<std::basic_string<char>>::push_back(int)'
vector<string> v; v.push_back(42);
^
Sure, it tells you that there’s a type mismatch, BUT imagine if this error was buried in a 500-line template backtrace and you can find an AI to parse that accurately.
\ Rust’s error messages are designed to be human-readable, which accidentally makes them perfect for AI consumption: each error contains the exact source location with line and column numbers, an explanation of which rule was violated, suggestions for how to fix it (when possible), and links to detailed documentation.
\ When Claude Code or Codex runs Cargo Check, it receives a structured error on which it can directly act. The feedback loop is measured in seconds, not debugging sessions.
One thing that made our development workflow significantly better at Sayna was investing in a correct CLAUDE. md file, which is essentially a guideline document that lives in your repository and gives AI coding tools context about your project structure, conventions, and best practices.
\ Specifically for Rust projects, you want to include:
\ The combination of Rust’s strict compiler with well-documented project guidelines creates an environment where AI tools can operate with high confidence; they know the rules, and the compiler enforces them.
At Sayna—WebSocket - handling, audio processing pipelines, real-time STT/TTS - provider abstraction, we use Rust for all the heavy lifting. These are exactly the kind of systems where memory safety and concurrency guarantees matter.
\ When Claude code refactors our WebSocket message handlers, it can’t eat it in an accidental way; when it changes our audio buffer management, it can’t create a use-after-free bug because the language simply does not allow it.
// The compiler ensures this audio buffer handling is safe
pub async fn process_audio_chunk(&self, chunk: Bytes) -> Result<()> {
let processor = self.processor.lock().await;
processor.feed(chunk)?;
while let Some(result) = processor.next_result().await {
self.tx.send(result).await?;
}
Ok(())
}
\ An AI tool might need several iterations to get the borrowing and lifetimes right, BUT each iteration is guided by specific compiler errors: no guessing, no hoping for the best.
OpenAI recently rewrote its Codex CLI entirely in Rust. It wasn’t just about performance - though that was definitely a factor - they explicitly mentioned that Rust eliminates entire classes of bugs at compile time - if OpenAI is betting on Rust for their own AI - coding infrastructure, it tells you something about where this is headed.
\ The security implications are also massive; codex now runs in sandboxed environments using Rust safety guarantees combined with OS isolation (Landlock on Linux, Sandbox-exec on macOS). When you have AI-generated code running on your machine, having compile-time security guarantees is not optional.
I won’t pretend that Rust is easy to learn because the ownership model takes time to internalize, and lifetimes can be frustrating when you are starting out— AI - coding tools are actually quite good at dealing with Rust’s sharp edges.
\ My favorite trick is to tell Claude Code to “fix the lifetimes” and let it figure out which combination of and, ref, as_ref(), and explicit lifetime annotations makes my code compile. At the same time, I concentrate on the actual logic and architecture.
// Before: Claude fix this
fn process(&self, data: Vec<String>) -> &str {
&data[0] // Won't compile - returning reference to local data
}
// After: Claude's solution
fn process(&self, data: &[String]) -> &str {
&data[0] // Works - borrowing from input parameter
}
\ This is actually a better way to learn Rust than struggling alone through compiler errors: you see patterns, you understand why certain approaches work, and the AI explains its reasoning when you ask.
If you’re considering using Claude Code or Codex for Rust development, here’s what I’d recommend:
We’re at an interesting inflection point: Rust is growing quickly in systems programming, and AI coding tools are actually becoming useful for production work; the combination creates something more than the sum of its parts.
\ At Sayna, our voice processing infrastructure handles real-time audio streams, multiple provider integrations, and complex state management: all built in Rust, with significant AI assistance, which means we can move faster without constantly worrying over memory bugs or race conditions.
\ If you’ve already tried Rust and found the learning curve too steep, give it another try with Claude Code or Codex as your pair programmer. The experience is different when you have an AI that can navigate ownership and borrowing patterns while you focus on building things.
\ The tools are finally catching up to the promise of the language.
\ © 2025 Tigran.tech created with passion by Tigran Bayburtsyan
