HackerNoonModify

2025-08-15 17:30:19

Every January, tech headlines promise “revolutionary changes,” but most fade before spring. But 2025 feels different. \n This year isn’t about a single breakthrough, it’s about multiple technologies maturing together: AI that understands human intent, quantum computers solving problems in minutes, and green tech becoming an economic necessity.

The challenge? Telling the difference between what’s real, what’s hype, and what will shape the next decade.

1. Context-Aware AI ✅ Real

Positive Impact:

• Turns customer service into personalized, meaningful interactions.
• Makes recommendations and decisions more relevant to each user.

Benefits:

• Boosts customer loyalty.
• Cuts down repetitive work for employees.
• Improves data-driven decision-making.

Real-World Use Case:

• Banking: AI assistants that detect if you’re asking about a loan because of a life event, offering personalized financing.
• Healthcare: Virtual health coaches that adapt advice based on your past records and lifestyle.

**Future Outlook: \ By 2027, expect AI to integrate deeply with emotional analytics understanding not just words but feelings.

2. Quantum Computing in Commercial Use ✅ Real

Positive Impact:

• Solves problems that traditional computers can’t handle in reasonable time.
• Opens doors to new materials, medicines, and energy solutions.

Benefits:

• Speeds up drug discovery.
• Optimizes global supply chains.
• Strengthens encryption systems.

Real-World Use Case:

• Logistics: DHL using quantum simulations to plan delivery routes in real time.
• Pharma: Roche running molecular simulations to find new cancer drug candidates.

**Future Outlook: \ By the early 2030s, quantum computing could become essential for climate modeling, predicting natural disasters with high accuracy.

3. Spatial Computing ⚡ Real + Hype

Positive Impact:

• Turns remote work, shopping, and education into immersive experiences.
• Improves industrial design with real-time 3D collaboration.

Benefits:

• Saves travel time and cost.
• Increases engagement in training and learning.
• Enables faster product prototyping.

Real-World Use Case:

• Retail: IKEA Place app letting users visualize furniture in their homes.
• Architecture: Teams designing buildings together in a shared virtual space.

**Future Outlook: \ Costs and device sizes will shrink, making spatial computing a standard part of smartphones and laptops by the next decade.

4. AI-Powered Cybersecurity ✅ Real

Positive Impact:

• Detects threats faster than human teams can react.
• Protects businesses against emerging AI-generated attacks.

Benefits:

• Reduces data breach risks.
• Cuts incident response time from hours to seconds.
• Learns and adapts to new threats continuously.

Real-World Use Case:

• Finance: JPMorgan Chase is using AI to detect suspicious transactions before they happen.
• Social Media: Platforms scanning deepfake content in real time to prevent misinformation.

**Future Outlook: \ AI will become the default “security analyst,” with human experts focused on strategy and high-stakes decision-making.

5. Green Tech as Core Strategy ✅ Real

Positive Impact:

• Helps companies meet sustainability goals.
• Reduces operational costs while lowering carbon footprints.

Benefits:

• Complies with environmental regulations.
• Attracts eco-conscious customers.
• Improves brand reputation.

Real-World Use Case:

• Energy: Google’s AI-managed data centers cut energy use by 40%.
• Manufacturing: Tesla using closed-loop recycling for battery materials.

**Future Outlook: \ By 2030, every major business will be expected to publish real-time sustainability data, powered by IoT and AI.

6. Self-Driving Cars ❌ Mostly Hype for 2025

Positive Impact (Eventually):

• Reduces accidents caused by human error.
• Increases transport accessibility for elderly and disabled populations.

Benefits (Once Mature):

• Frees up commute time.
• Lowers transportation costs.

Real-World Use Case:

• Public Transport: Waymo running autonomous taxi pilots in select cities.

Future Outlook: Full autonomy is still years away due to regulatory, safety, and infrastructure hurdles expect incremental progress instead of overnight change.

7. “AI Replacing All Jobs” ❌ Hype

Positive Impact of Reality:

• AI is enhancing productivity, not replacing all human roles.
• Frees workers from repetitive tasks, enabling focus on creativity and strategy.

Benefits:

• Higher efficiency.
• Reduced burnout.

Real-World Use Case:

• Media: Journalists using AI to summarize large reports quickly, then adding their human analysis.
• Legal: Law firms using AI to sort case documents before lawyers review them.

Future Outlook: Workplaces will shift to “AI-augmented teams,” with human expertise as the differentiator.

Final Thought

2025 isn’t defined by one “next big thing.” It’s the collision of multiple breakthroughs:AI, quantum, biotech, green tech, and immersive computing happening at once. \n The winners in this decade will be those whocombine these technologies, not just adopt them individually.

The hype cycles will keep spinning, but the real impact will come from leaders who know how to filter the noise, invest in what’s working, and prepare for what’s next.

\ **💡 Ready to ride the wave of 2025’s biggest tech shifts? \ Don’t just watch the future happen, be part of it. let’s build it together. \n

:::info Would you like to take a stab at answering some of these questions? The link for the template is HERE, just start writing! Interested in what others had to say in their answers? Click HERE. Interested in reading the content from all of our writing prompts? Click HERE.

:::

\

2025-08-15 17:22:24

Multicloud is no longer optional—89% of orgs use it in 2025. This guide breaks down how to navigate its complexity, the evolution of cross-cloud platforms, and best practices for architecture, governance, and cost control. With N2W, IT teams can streamline cross-cloud backup and DR, reduce spend, and future-proof operations.

2025-08-15 15:29:06

Every class is a petri dish for state corruption. You cannot make a stateful class truly thread-safe. Classes bad; Trust me.

2025-08-15 15:27:38

\

Background

When monitoring the running status of the system and troubleshooting complex issues, logs have long served as an indispensable observability method. Scientific local log management strategies not only retain more complete historical records locally and minimize performance overhead but also facilitate log collection and subsequent analysis. However, in actual O&M, we often encounter counterexamples. The collection problems caused by such management defects cannot be perfectly solved by mainstream collection tools such as LoongCollector (formerly iLogtail), Filebeat, FluentBit, Vector, and OpenTelemetry Collector. The best practice is to solve the root cause. Here we summarize our experience, hoping to provide some inspiration and collectively enhance log utility for all.

1. Using the copy truncate mode for log rotation may cause log loss or duplicate collection due to non-atomic operations and new file creation.

The principle of using the copy truncate mode of Logrotate to rotate logs is to first copy the original log file and then truncate it. This method raises the following issues:

1. New files generated by the copy operation may be repeatedly collected as new content. Due to inode changes in the file system, the collector may fail to correctly identify this as the rotated old file.
2. Logs generated between copy and truncate operations may be lost. There is a time window between these two operations, during which the content written exists neither in the copied file nor in the original file (as it will be cleared by the truncate operation).
3. The truncate operation may reduce the file size and change the header content. Shrinking the file or changing its header signature will cause the collector to misjudge it as a new file, resulting in duplicate collection.

Therefore, the copy truncate mode may lead to issues like duplicate log collection, content loss, or inconsistency.

It is recommended to use the create mode for log rotation, that is, to create a new file and rename the old file, which ensures file integrity and continuity. If unavoidable, use the exact path name when configuring the collection settings.

2. Using NAS or OSS for log storage may cause log collection truncation or termination due to inconsistent metadata and poor file listing performance.

Network-attached storage (NAS) typically employs an eventual consistency model, which is a common design in distributed systems. In real-time collection scenarios, this may cause the following issues:

1. Inconsistency between object metadata and actual content. Due to eventual consistency, metadata such as file size may be updated before the actual content.
2. Read operations returning file holes. If the metadata shows that the file has grown, but the actual content has not been synchronized, the read operation may return 0 characters (file holes).
3. Data latency. The results of write operations may not be immediately visible to read operations, causing collection latency.
4. Data loss. NAS does not support inotify, and objects are listed at a low speed, so files may not be detected, resulting in data loss.

These issues may cause the collected data to be inconsistent with the final content.

It is recommended to use EBS and use local disks for on-premises servers to ensure the efficiency and consistency of log reading and writing. If unavoidable, implement the compatibility logic for exception logs on the consumer.

3. Multi-process log writing may cause incomplete data collection due to mutual data overwriting.

It is a common but not recommended practice for multiple processes to write to the same log file concurrently, which may lead to the following problems:

1. Interleaved file content. Writes by multiple processes may intersect with each other, causing disorder in log entries.
2. Incomplete collection. When a write event occurs on the file, the collector starts collecting data. However, if other processes continue to write data during the collection process, these newly written contents may be skipped.
3. File lock contention. Multi-process writes may cause file lock contention, which affects write performance and reliability.

This pattern may result in incomplete data collection that deviates from the final file contents.

It is recommended that multiple processes write their respective files to ensure log integrity and order. If unavoidable, implement the compatibility logic for exception logs on the consumer.

4. Creating file holes to release log file space may cause duplicate log collection or data loss due to changes in the file signature and content.

Releasing log file space by creating holes in the file header is a risky practice for the following reasons:

1. Changed file signature. LoongCollector (formerly iLogtail) additionally uses the header content of the file as the basis for determining file uniqueness to prevent missed collection from inode reuse. Creating holes may change this signature, causing the collector to misjudge it as a new file.
2. Data integrity issues. Creating holes essentially replaces the original content with 0 characters, which may result in the loss of important historical logs.
3. File system fragmentation. Frequent creation of holes may lead to file system fragmentation, degrading read and write performance.

This practice may result in duplicate data collection and loss of historical data.

It is recommended to use the standard log rotation mechanism to manage log file size, such as using the Logrotate tool for regular log rotation, which ensures log integrity and traceability. If unavoidable, we recommend that you use fallocate instead of truncate or dd, and implement compatibility logic for exception logs on the consumer.

5. Frequent overwriting of files may result in incomplete or inconsistent collected data due to constantly changing file content.

Frequently overwriting the entire log file is an insecure log management method. It may cause the following issues:

1. Inconsistent metadata and content. During the overwriting process, metadata such as the file size may be updated before the actual content. As a result, the collector reads incomplete or inconsistent content.
2. Data loss risk. If overwrite operations occur during log collection, the collected data may be disordered or lost.
3. Difficulty retaining historical data. Frequent overwriting causes failure to retain historical logs, which is not conducive to issue tracing and analysis. This practice may result in inconsistency between the collected content and the final file content, or complete loss of file content.

It is recommended to record logs in append mode and use the log rotation mechanism to manage the file size. If unavoidable, implement the compatibility logic for exception logs on the consumer.

6. Saving files edited with Vim may cause duplicate log collection due to creating new files to replace the original ones.

When you use Vim to edit and save a file, its saving mechanism can cause the following issues:

1. The inode change. When Vim creates a new file to replace the original one, the new file has a different inode from the original, which may cause the collector to misjudge it as a new file.
2. Changed file signature. The header content of the new file may differ from that of the original file, which changes the file signature. As a result, the collector cannot correctly identify the file.
3. File content loss. When Vim replaces the file, the writing program may not switch to the newly saved log file, potentially resulting in loss of log content.

This editing mode may lead to duplicate log collection or data loss.

If you only need to view logs, we recommend that you use read-only tools such as less and grep. If unavoidable, implement deduplication and exception handling logic on the consumer.

Summary

Log is the "black box" of system operations, and its management quality directly affects the troubleshooting efficiency and system reliability. By avoiding the anti-patterns mentioned in this article and following the best practices, such as using Logstore rotation, local disk writing, and single-threaded appending, you can significantly lower log collection risks and improve observability. It is hoped that this article can provide practical references for teams to build a robust and efficient log management system.

2025-08-15 15:22:42

Lately, the crypto chatterbox has been buzzing with two conflicting refrains: “Alt season is coming!” and, at other times, “Alt season is over.” As both a curious observer and someone who wants clear insight, I wondered, “Which one is actually true?” So I rolled up my sleeves, looked at the data, and here’s what I found, fresh from real, credible indicators.

\

:::warning Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR

:::

Understanding the Altcoin Season Index (Your Reality Check)

\

\ Think of the Altcoin Season Index (ASI) as a scorecard that compares altcoins to Bitcoin. It reflects how many of the top altcoins have outpaced Bitcoin over the last 90 days. If 75 percent of them did, that often signals a full-blown altcoin season. When the score dips below 25, Bitcoin is calling the shots.

What have the numbers said over the past months?

• June 2025: The ASI dropped to about 12, meaning almost all altcoins trailed behind Bitcoin.
• Now (mid-August 2025): The index has climbed to around 41, which still places us firmly in Bitcoin Season, not altcoin season.

So, while a rising index hints at rotation, altcoins are not yet leading the charge.

Bitcoin Dominance: The Bigger Picture

\

\ Shifting our gaze, Bitcoin Dominance (BTC.D) measures what portion of the total crypto market cap Bitcoin holds. When BTC.D shrinks, that often signals money moving into altcoins, fueling their rise.

• Recently, Bitcoin’s share of the market dropped by around 5 percent, a sign that altcoins are gaining some attention.
• Additionally, some analysts noted classic bullish chart formations like cup-and-handle in altcoin market cap charts, hinting that altcoin season might be creeping closer.

That said, even though Bitcoin is loosening its grip a little, altcoins have not yet taken the spotlight.

Memes, Risk, and Warnings Along the Way

Even though data does not confirm alt season yet, there is a lot of excitement brewing.

• A recent article put it plainly: “Altcoin season looms”, suggesting optimism, but it also reminded readers that the space is highly speculative.
• Other outlets noted vulnerabilities. Altcoins like Ether, XRP, Solana, and Dogecoin saw volatility, and some analysts attributed pullbacks to regulatory uncertainty and Bitcoin’s price plateau.

In other words, yes, there is action. But hype does not equal momentum, and risks remain high.

So, Who’s Right? “Coming” or “Over”?

\

\

“Alt season is coming”

That view leans on a recovering Altcoin Season Index, falling BTC dominance, and bullish patterns on altcoin charts. If altcoins rally, this path could lead somewhere.

“Alt season is over”

That perspective leans on still-low ASI scores and current dominance patterns. After a brief surge or even a false breakout, altcoins might fade again.

A Story of a Wave in Analogy

Let me borrow a surfer’s metaphor. Picture Bitcoin as a large wave pushing into the shore first. Then, smaller waves, representing altcoins, follow behind. Sometimes, those follow-on waves come strong. Other times, they just trickle in.

Right now, we are seeing hints of rising tide, some splashes and boarders heading toward altcoins, but we have not yet seen the full, crashing wave that signifies altcoin season in full bloom.

What Should You Do If You Are New to This

\

\

1. Track the ASI. Watch for a move above 75, which historically signals altcoin season.
2. Watch Bitcoin Dominance. A clear, sustained drop can further fuel rotations.
3. Be selective. If you choose to explore altcoins now, go for names with use cases, development, and real value.
4. Manage risk. Alt seasons can be short and sharp. Have a clear exit plan and avoid chasing hype.

So..

In the past three months, the crypto landscape has shifted, but not enough to confirm a full altcoin season. While the Altcoin Season Index is rising and BTC dominance is easing, we still linger in Bitcoin’s season. Yet, there is undeniable buzz. The setup feels like the calm before the ride, not the ride itself.

So, if someone says, “Alt season is coming,” they are not wrong, but the season has not fully arrived. And if someone says, “Alt season is over,” they are getting ahead of themselves. Right now, we are in an early rotation stage, and from where I sit, the altcoin wave might soon pick up. Patience and a measured approach will be your friend.

If there’s something you want me to cover next, just let me know. You can follow me here on my website to get my latest updates as soon as they drop! You can also contact me through X @AskaraJr and Linkedin

\

2025-08-15 15:20:39

Here’s a list of practical strategies for designing courses and assessments that make it harder for students to misuse AI while still encouraging authentic learning.

AI has created a nightmare scenario for educators — and trying to police it is a losing battle.

The good news is that these challenges can be addressed by adapting your curriculum or syllabus to limit AI cheating while encouraging the ethical use of AI. While this may be more difficult in some subjects, it can also be surprisingly easy if you choose the right methods and tools.

Side note — Interestingly, I used AI to help me develop this guide, but most of the suggestions it provided were not AI proof. And it misspelled the text in the graphic. Acts of self preservation perhaps. 🙂

1. General Course Design & Delivery

• Have an AI policy — Clearly state when and how AI tools can and cannot be used.  See section 9 for rationale you should share with your students.
• Pen-and-paper or offline assessments — Require students to complete work without digital tools, using only pen, pencil, or physical materials.
• Prioritize process over product — Make drafts, logs, reflections, and checkpoints worth as much as the final submission.
• Vary assessment types — Combine written, oral, practical, reflective, and collaborative work.
• Frequent low-stakes checks — Short in-class quizzes, verbal questions, or quick exercises to confirm understanding.
• Use personalization — Require class-specific, local, or personal examples that AI won’t have access to.
• Rotate and refresh assignments — Change questions, prompts, and data sets each term.
• AI-inclusive assignments — When appropriate, require students to use AI but also critique, improve, or fact-check its output.

2. Validation & Assessment Strategies

• Proctored or supervised assessments — Use in-person or secure online proctoring for key checkpoints. Enforce room sweeps, restricted devices, and dynamic versions to reduce collusion.
• Live conversations — Have short one-on-one or small-group discussions about submitted work.
• Spot checks — Randomly ask a few students to explain their reasoning or recreate parts of their work.
• Version history — Require work to be done in cloud-based tools with change-tracking enabled.
• Think-aloud exercises — Students narrate their problem-solving process in real-time.
• Video presentations — Students record themselves presenting without a teleprompter (start with a wide view of the environment and no edits).

3. Experiential & Team-Based Learning

• Projects over high-stakes exams — Shift weight to applied, multi-step projects.
• Games and simulations — Use tools that require interactive decision-making and direct student engagement. (Examples of programs I create are at GoVenture.net )
• Team-based activities — Have students collaborate and keep a shared journal or log of their contributions.

4. Math & Science Approaches

• Hands-on tasks — Use real-world objects for measurement, calculation, or experimentation. Require photos of the process.
• Home-based experiments — Students build models, conduct small experiments, record data, and show step-by-step progress.
• Unique variables — Give each student or group slightly different input values or datasets.
• Local data integration — Require problems to be solved using nearby environmental, community, or current-event data.

5. Writing Approaches

• Pen-and-paper process — Students submit photos of brainstorming notes, outlines, and multiple drafts.
• Obscure or nuanced topics — Use prompts that AI may misinterpret or lack data for.
• In-class source referencing — Require citations from recent lectures, guest speakers, or materials not online.
• Personal integration — Combine personal experiences with course concepts.
• Meta-reflections — Have students submit a short statement explaining their writing choices and revisions.
• Keystroke-tracking tools — Some apps can log writing sessions to reveal pasted text, though this moves toward policing and should be used cautiously.

6. Arts & Design Approaches

• Document the creative process — Require photos or screen recordings of work in progress (sketches, drafts, iterations).
• Physical creation — Include at least one element that must be produced by hand (sculpture, painting, physical prototype).
• Style emulation — Have students replicate a specific style taught in class, which AI tools may not match well.
• Peer critique sessions — Students present work for live feedback and Q&A.

7. Computer Science & Technology Approaches

• Code-from-scratch challenges — Assign problems that must be solved without access to online code repositories. Verify via live coding demos.
• Oral walkthroughs — Students explain their code logic and design decisions verbally.
• Debugging tasks — Provide partially broken code to fix, requiring explanation of the errors.
• Version control evidence — Require use of Git or similar tools so you can review commit history.

8. Business, Social Sciences & Humanities Approaches

• Case study personalization — Have students analyze a case from their own community or workplace.
• Role-play simulations — Assign scenarios where students act out negotiations, debates, or leadership decisions. (Examples of programs I create are at GoVenture.net )
• Current event integration — Use topics from the last few days or weeks to make AI-prewritten responses less likely.
• Field research — Require original quotes, interviews, or survey data collected by the student.

9. Why Some Skills Must Be Developed Without AI Assistance

While AI can be a powerful aid, there are skills that lose depth, resilience, and transferability if they are always practiced with AI support.

• Cognitive Foundations – Skills like critical thinking, problem-solving, and logical reasoning are strengthened through the mental effort of working problems out independently. Overreliance on AI can short-circuit this development.
• Creativity and Originality – True creative ability requires generating and refining ideas from scratch. If AI supplies the first or best ideas, students may never develop their own voice or style.
• Retention and Recall – Without personally struggling through concepts, students are less likely to remember and apply them later, especially in time-pressured or offline situations.
• Judgment and Decision-Making – Many real-world situations require weighing incomplete, conflicting, or ambiguous information. Practicing this without AI helps students build confidence and intuition.
• Skill Transfer – Mastery in one context (e.g., writing without AI) often supports performance in other contexts (e.g., verbal explanations, live problem-solving) where AI may not be available or allowed.

For these reasons, certain learning activities should be deliberately designed so that students engage without AI assistance — ensuring they develop durable, self-sustaining capabilities alongside their AI literacy.

\ \