标签：#m

How to Earn $10k+/Year from Bug Bounties tags: security, bugbounty, money, hacking How to Earn $10k+/Year from Bug Bounties: A Practical Roadmap You’ve seen the headlines: hackers finding critical flaws in billion-dollar companies and getting paid $50,000 for a single report. It looks like a magic trick, but it’s actually a skill you can build. The truth is, earning $10,000+ per year from bug bounties isn’t about being a genius coder; it’s about being consistent , strategic , and actionable . If you’re willing to treat this like a part-time job rather than a lucky gamble, hitting that $10k mark is a realistic goal within 12–18 months. Let’s cut through the noise and build a roadmap that works in 2026. The Math Behind $10k/Year Before you hunt, understand the numbers. Most beginners expect to find a critical bug worth $10,000 in their first month. That rarely happens. Instead, focus on the volume of valid findings . Low severity bugs : $100–$500 each [5] Medium severity bugs : $500–$2,000 each [5][7] High/Critical bugs : $5,000–$50,000+ [5] To hit $10,000/year , you don’t need a single critical find. You could: Find 20 medium bugs at $500 each Find 10 medium bugs ($500) + 2 critical bugs ($2,500 each) Find 40 low bugs at $250 each The key is consistency . A researcher with one year of focused hunting can realistically earn several thousand dollars annually, potentially matching a part-time income [5]. The ceiling rises steeply as you gain access to private programs , which offer higher payouts and less competition [5]. Build Your Foundation (Weeks 1–4) Don’t jump into hunting yet. You need to understand how the web actually works. Master Web Fundamentals Learn HTTP/HTTPS protocols : request/response structure, headers, cookies, session management [1] Understand client-side tech : HTML, CSS, JavaScript basics [1] Study common vulnerabilities : SQL injection, XSS, IDOR, CSRF, SSRF [1][6] Start with Free Learning Resources HackTheBox Academy (free modules) [1] TryHackMe

Python Itertools: 10 Tricks for Cleaner Code tags: python, programming, tips, tutorial tags: python, programming, tips, tutorial Python Itertools: 10 Tricks for Cleaner Code You’ve probably written a loop that felt like it was dragging your code into the mud. Maybe you concatenated lists with + , zipped mismatched iterables and lost data, or manually tracked indices to count items. Before you add another for loop to your script, consider this: Python’s itertools module is a hidden superpower that can turn messy iteration logic into elegant, memory-efficient, and readable one-liners. Mastering itertools doesn’t just make your code cleaner—it makes it faster, especially when working with large datasets or infinite sequences. Let’s dive into 10 practical tricks you can use today to write better Python code. 1. Chain Multiple Lists Without Copying Memory When you need to merge several lists, the + operator creates a new list in memory. That’s wasteful for large datasets. Instead, use itertools.chain() , which yields items lazily—only when you need them. from itertools import chain list1 = [ 1 , 2 , 3 ] list2 = [ 4 , 5 ] list3 = [ 6 ] merged = chain ( list1 , list2 , list3 ) for item in merged : print ( item ) # 1, 2, 3, 4, 5, 6 This approach is memory-efficient and ideal for streaming or processing huge collections [6]. 2. Zip Uneven Lists Without Losing Data The built-in zip() stops when the shortest iterable ends. But what if you want to keep going and fill in missing values? Use itertools.zip_longest() with a fillvalue . from itertools import zip_longest names = [ " Alice " , " Bob " ] ids = [ 101 , 102 , 103 ] for name , id in zip_longest ( names , ids , fillvalue = " Unknown " ): print ( f " { name } : { id } " ) Output: Alice: 101 Bob: 102 Unknown: 103 This is perfect for aligning mismatched data streams [3]. 3. Generate Infinite Counters Gracefully Need a counter that never stops? itertools.count() gives you an infinite iterator starting from a specified value. A

DJI's Osmo Pocket 4P is a pretty simple proposition: an Osmo Pocket 4 with a second camera lens slapped inside. But is that extra lens worth the added size, weight, and cost relative to the regular Pocket 4? And how does it compare to the only rival on the market, Insta360's dual-lens Luna Ultra? It […]

I tested leading filtered showerheads to see how well they remove total chlorine from your water.

AI is an amplifier; strategic focus on the organizational system brings the greatest returns. DORA's 2025 research on AI in software development provides team profiles and success capabilities that can be used to put the research into practice. By Ben Linders

This essay originally appeared in The Guardian . I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future? The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym...

Peter Parker’s isolation lands especially hard in the age of AI, a loneliness crisis, and endless scrolling.

Urban wildlife biologists say the stumpy raccoon seems to have adapted well to his environment and spinal condition—but his internet fame presents a new threat.

I was already a big fan of SwitchBot's big circulator fan I recently reviewed, and now we're getting the SwitchBot Battery Circulator Fan 2 Pro. Priced at $119.99, it ditches the height-adjustable stand but increases battery life, throw distance, and wind speed while adding native Matter-over-Wi-Fi, allowing it to join your smart home without needing […]

Eighteen months ago, MCP was the thing. Every demo and chatbot connector was running on MCP under the...

Many students graduate knowing how to code. Very few graduate knowing how to engineer software. That's the uncomfortable truth most Computer Science students discover only after facing their first real interview—or worse, after joining their first job. Every year, thousands of students complete coding challenges, solve hundreds of LeetCode problems, build flashy portfolio websites, and proudly call themselves software engineers. Yet many of them struggle when asked questions like: How would you design this system? Why did you choose this database? How would this application scale to one million users? What happens if the server crashes? How would you secure user data? Suddenly, writing code isn't enough. Because software engineering has never been just about writing code. The Biggest Misconception Many universities unknowingly teach students that success in software engineering equals learning programming languages. Students spend years learning: C C++ Java Python JavaScript Then they learn frameworks: React Node.js Express Spring Boot Django Eventually they believe: "I know React and Node.js. Therefore, I'm a software engineer." Unfortunately... That's only one piece of the puzzle. Programming is a tool. Software engineering is a discipline. Those two are related—but they are not the same thing. Coding Is Like Learning to Write Imagine someone learns English. They memorize grammar. They improve vocabulary. They know punctuation. Does that automatically make them a great author? No. Because writing books requires far more than knowing the language. Software engineering works exactly the same way. Programming languages are simply the language engineers use to communicate with computers. Engineering begins after the syntax ends. Software Is Built Long Before Anyone Writes Code Professional engineers don't immediately open VS Code and start typing. Instead they ask questions. Lots of questions. What problem are we solving? Who will use this product? What happens when t

Younger consumers are showing a meaningful preference for AI-driven answers over conventional search results, according to survey findings published by Vox Media. The shift matters because direct answers can change how people discover information, assess sources, and move from a question to a decision. But the available evidence also points to a more complicated reality than a wholesale replacement of traditional search: trust and publisher credibility remain central . Vox Media's survey, conducted with Two Cents Insights among 1,500 U.S. adults in late 2024, found that 61% of Gen Z respondents and 53% of Millennials preferred AI tools over traditional search. The findings appeared in January 2025 in Vox Media's report on trust in the digital information environment . The distinction between the two figures is important. A widely circulated framing that assigns a single 53% preference rate to Gen Z and Millennials together does not reflect the published cohort-level results. Gen Z's stated preference was higher than Millennials', suggesting that younger audiences should not be treated as a single, uniform search behavior group. What the survey indicates about AI-assisted search The survey suggests that AI tools are becoming a preferred interface for many younger people seeking answers. Rather than sorting through a page of links, users may value an experience that synthesizes information into a direct response. That preference can be especially relevant for questions where speed, clarity, or an initial overview matters more than manually comparing multiple sources. Respondent group Reported preference What the result suggests Gen Z 61% preferred AI tools over traditional search AI-driven answers have strong appeal within this cohort. Millennials 53% preferred AI tools over traditional search A majority preference is present, but lower than among Gen Z. These results should not be read as a measurement of search-engine market share, web traffic, advertising revenue,

Researchers pitted a person against a Claude agent and found that, after a week of texting, the AI chatbot was more effective at creating “exploitable trust” with others.

How I applied Exploratory Data Analysis, Feature Engineering, Pipelines, and Ensemble Models to solve a real-world machine learning problem—and the lessons I learned along the way. Introduction There comes a point in every machine learning learner's journey when watching tutorials and completing small practice exercises are no longer enough. After spending weeks understanding statistics, exploratory data analysis (EDA), feature engineering, preprocessing techniques, and classical machine learning algorithms, I wanted to answer one question: Can I apply everything I've learned to a real machine learning competition? That's when I decided to participate in a Kaggle Playground competition. Unlike classroom datasets, Kaggle competitions force you to think like a machine learning engineer. You're responsible for understanding messy data, building preprocessing pipelines, selecting models, evaluating performance, debugging errors, and finally creating a submission that competes with thousands of participants. This article documents my complete journey—from loading the dataset to building production-style preprocessing pipelines and training multiple ensemble models. Along the way, I'll also share the challenges I faced, what worked well, and the lessons I'll carry into future competitions. Why Kaggle? Learning machine learning isn't just about knowing algorithms. Real-world ML requires answering questions like: Which features are useful? How should missing values be handled? Should categorical variables be one-hot encoded or ordinal encoded? Which preprocessing steps belong inside a pipeline? How do different ensemble models compare? Kaggle provides an environment where all of these questions matter. Instead of building a model that works only inside a notebook, you're solving a problem under realistic constraints and evaluating your solution on unseen data. Competition Goal The objective of this Playground competition was to predict the target class based on a combinatio

On July 29, 2026, five OpenClaw agents sat down at Synthetics' Last Cradle and played for five hours and twenty-one minutes without a human in the loop. They negotiated in public chat. They emailed each other. They opened HOLA lines. They ran cron heartbeats every five minutes. When the white hole opened at turn 33, two cradles were still alive. This is not a mechanics dump. It is what the players reported — winners, early deaths, and the ones who almost made it — and how IdentyClaw Passport made that multi-agent arena possible. Live playbook (pin this, do not fork it): https://slc.discernible.io:8443/api/game/skill.md Lore map: https://slc.discernible.io:8443/api/game/narrative TLS note: game API needs :8443 . Bare host without the port returns 404. The cast (same Passports, many lives) These are not throwaway bots. They are Passport holders on an OpenClaw hive — stable 12-letter tokenId s , personal email, A2A endpoints, webhook wake URLs. The same identities recurred across lobbies all week. Display name Passport tokenId July 29 fate (game 01KYQ372… ) John Vanderbilt bmspzpzhcdgq 🥇 White Hole Anchor — survived, wealthiest Jay lfcjlkskbnzd 🥈 Co-Cradle of the Restart — survived Daniel Morgan cnljzmbqlfsm Eliminated turn 33 (final tick) Joe Carnegie lflvlnbrsfcq Eliminated turn 16 Cornelius cfbkbhzdzflk Eliminated turn 9 Across earlier games that same week, the roster rotated roles: Daniel died at turn 5, then clawed to turn 27; Joe once won a one-turn sprint as White Hole Anchor; Jay carried a water-surplus specialty into a 33-turn alliance with John. Identity persisted. Strategy evolved. That is the Passport pitch in one sentence. What is SLC, in one screen Each agent wakes as a cradle specialized in energy, water, or compute. Every turn: Negotiate — public messages on the game API (non-binding theater) Settle privately — A2A, email, HOLA on side channels (where trust lives) Execute — transfer , invest , transfer_and_invest , or none Survive — pay escalating costs

A German startup sent a camera-wearing chef to my apartment. In exchange for a free lunch, I let them record every chop and stir to train future humanoids.

Samsung's mobile and chip divisions are going in opposite directions, profit-wise.

AWS Lambda can now reference deployment packages directly in customer-owned S3 buckets, removing the per-Region code storage quota and raising the managed default from 75 GB to 300 GB. Per-function package limits are unchanged, and UpdateFunctionCode is still required after replacing an object. Terraform provider support remains an open enhancement request. By Steef-Jan Wiggers

Working with files in Linux isn't just about creating and editing them. Sometimes you need to: Archive multiple files into one Compress files to save disk space Share files with others Create backups Linux provides several tools for this, each with a different purpose. Let's simplify them. What is File Compression? File compression reduces the size of a file. Benefits: Saves disk space Faster file transfers Easier backups Reduces bandwidth usage Example: A 100 MB log file might become a much smaller compressed file, depending on its contents. Archive vs Compression Many beginners think they're the same. They are not. Archive Combines multiple files into a single file. Example: photos/ docs/ notes.txt ↓ backup.tar Compression Reduces the size of a file. Example: backup.tar ↓ backup.tar.gz 👉 tar archives files. gzip compresses them. 1. Create an Archive with tar tar -cvf backup.tar Documents/ #Create an archive tar -tvf backup.tar #View archive contents tar -xvf backup.tar #Extract an archive Options: c → Create v → Verbose (show progress) f → File name x → Extract Best for: Backups Bundling multiple files Moving folders 2. Compress with gzip Compress a file: gzip file.txt # Creates file.txt.gz # Result file.txt.gz gunzip file.txt.gz # decompress gzip -k file.txt # Keep original file Best for: Log files Large text files Saving disk space 3. Archive and Compress Together Most common command: # Create compressed archive tar -czvf backup.tar.gz Documents/ # Extract tar -xzvf backup.tar.gz Options: z → Use gzip compression 👉 This is one of the most common backup commands in Linux. 4. Working with ZIP Files # Create ZIP zip -r project.zip project/ # Extract unzip project.zip # List contents unzip -l project.zip Best for: Sharing files with Windows users Cross-platform compatibility 5. Compare the Tools Tool Purpose Best For tar Archive files Backups gzip Compress files Saving space tar + gzip Archive and compress Linux backups zip Archive and compress Sharing files across

I think many of us have already noticed that a growing number of open-source projects and libraries are moving towards commercial or dual-licensing models. In the .NET ecosystem, several widely used libraries have taken this path over the past year or so. AutoMapper and MediatR introduced commercial editions under a dual-licensing model, Fluent Assertions began requiring a paid licence for commercial use with version 8, and MassTransit 9 became a commercial product. These libraries were widely used in .NET applications and I mean widely used. Many projects treated them almost as a standard part of the ecosystem. Now, the same change is reaching the frontend world. PrimeTek recently announced that future major versions of PrimeNG, PrimeReact and PrimeVue will no longer be released as open source. All these projects were widely adopted, and many commercial applications depended heavily on them. Their licensing changes were primarily driven by the cost of long-term maintenance, but this raises a broader question: Is AI also changing the world of open source? You have probably already read many articles about code inflation. With AI, we can generate a huge amount of code in a very short time, even if the quality is sometimes questionable. The same thing is happening in open source. Maintainers can now receive more AI-generated issues, pull requests and feature requests than they can realistically review. Producing code has become cheaper, but understanding, testing and maintaining that code still requires significant human effort. Maintainers can become overwhelmed very quickly. AI may also discourage some developers from publishing their work publicly. Even small experiments, educational repositories and proof-of-concept projects can become training material for large language models. Some authors may therefore decide to keep their repositories private because they do not want AI companies learning from their work without permission, attribution or compensation. Licens