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Let’s say you have a job interview in a few days. How should you prepare for it so that you can make an informed decision about joining the company, as well as make sure that your interests are taken care of? Prepare your questions An interview is a two-way street: it’s not only the company investigating you as a potential employee; in addition, you are investigating the company as a potential employer. Hopefully, you have some other options available—such as staying at your current place or trying out other companies interested in you. In the best case, you’ll have the flexibility to decide whether you want to follow through with the company or not. To learn about the potential employer, you need to ask questions. The question will depend on what’s important for you at the current stage of your career. Some things I would like to know before deciding to join a new company are as follows: What is their development process? If they use some standardized methodology such as Scrum, I would like to hear how they implement it. Especially whether they have Scrum Master and Product Owner roles covered—and whether those people are easily reachable. I would also ask if they do retrospective meetings. How do they do quality automatization: things such as unit tests, e2e, CI & code-style enforcing? For things that they don't have, I would ask if they would like to see it in the project. I would ask about their product and how they do product design/discovery. Life–work balance questions inspired by : How do they deal with emergencies? How often is there a need to receive a call or respond to messages outside working hours? Healthy Software Developer’s video how long do employees usually work? Salary negotiation The point of going to the interview is to get to the salary negotiation. The result of the negotiation will have a great impact on your life for the whole time you spend at the company—it’s a smart move to prepare well. The earlier you start, the better—as you will have more time to investigate, ask questions, and plan your strategy. The best resource for preparing for an interview in IT is . You can buy the complete e-book, read the chapters online, or get the key advice from the bite-size articles on the website. Fearless Salary Negotiation Research compensation Before you start negotiation, figure out the current situation in the market. A little bit of time spent on systematic investigation can give you a much more precise picture than anecdotes heard here or there. Check out this . chapter on market value estimation Decide your minimum Great advice is to decide on the minimum pay that you’ll accept you start speaking with the company. It could happen that during the various rounds of interviews, they will manage to the company very well or that the effort you have already invested into the company will make you more flexible than you should be. In those cases, having the minimum salary decided ahead of time can be very helpful. . before sell Relevant chapter from the book here Think openly about your options. As you negotiate with the company, it’s good to be flexible about the options you consider. If you are asking for more money than they have budgeted for, you can propose reduced hours with the same hourly rate that you are happy with. Similarly, commuting to the office can easily take 1–2 hours of your day, so a lower salary could be worth it if it comes with guaranteed remote work. Practice interview Try role-playing an interview with someone. If you can handle it, you can ask them to be tough on you—stricter than what you would expect from a real interview. Ideally, you’ll have a tough, low-stakes interview that would make you relaxed during the real thing. Investigate the company Learn about the company—all you can do before the meeting, that is. Check their website, their LinkedIn page, and their key employees. Play around with their product if you can. In short, spend an hour or two trying to learn as much as you can about the company. It will help you ask important questions and make a good impression. Summary A bit of preparation can go a long way. As developers, we often think about the technical part of the interview, but learning about the company and preparing for salary negotiation can help us even more. We can filter out companies we shouldn’t join, and we can make sure that we get as good a deal as possible. Also published . here

Nailing Your Job Interview: Preparation, Research, and Looking Out For Your Best Interests

When we think of debugging, we think of breakpoints in IDEs, stepping over, inspecting variables, etc. However, there are instances where stepping outside the conventional confines of an IDE becomes essential to track and resolve complex issues. This is where tools like DTrace come into play, offering a more nuanced and powerful approach to debugging than traditional methods. This blog post delves into the intricacies of DTrace, an innovative tool that has reshaped the landscape of and system analysis. debugging https://youtu.be/3M0AhZnVoUk?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my . If you want to get back to Java after a while, check out my Debugging book Java Basics book Java 8 to 21 book . DTrace Overview DTrace was first introduced by Sun Microsystems in 2004. DTrace quickly garnered attention for its groundbreaking approach to dynamic system tracing. Originally developed for Solaris, it has since been ported to various platforms, including MacOS, Windows, and Linux. DTrace stands out as a dynamic tracing framework that enables deep inspection of live systems – from operating systems to running applications. Its capacity to provide real-time insights into system and application behavior without significant performance degradation marks it as a revolutionary tool in the domain of system diagnostics and debugging. Understanding DTrace’s Capabilities DTrace, short for Dynamic Tracing, is a comprehensive toolkit for real-time system monitoring and debugging, offering an array of capabilities that span across different levels of system operation. Its versatility lies in its ability to provide insights into both high-level system performance and detailed process-level activities. System Monitoring and Analysis At its core, DTrace excels in monitoring various system-level operations. It can trace system calls, file system activities, and network operations. This enables developers and system administrators to observe the interactions between the operating system and the applications running on it. For instance, DTrace can identify which files a process accesses, monitor network requests, and even trace system calls to provide a detailed view of what's happening within the system. Process and Performance Analysis Beyond system-level monitoring, DTrace is particularly adept at dissecting individual processes. It can provide detailed information about process execution, including CPU and memory usage, helping to pinpoint performance bottlenecks or memory leaks. This granular level of detail is invaluable for performance tuning and debugging complex software issues. Customizability and Flexibility One of the most powerful aspects of DTrace is its customizability. With a scripting language based on C syntax, DTrace allows the creation of customized scripts to probe specific aspects of system behavior. This flexibility means that it can be adapted to a wide range of debugging scenarios, making it a versatile tool in a developer’s arsenal. Real-World Applications In practical terms, DTrace can be used to diagnose elusive performance issues, track down resource leaks, or understand complex interactions between different system components. For example, it can be used to determine the cause of a slow file operation, analyze the reasons behind a process crash, or understand the system impact of a new software deployment. Performance and Compatibility of DTrace A standout feature of DTrace is its ability to operate with remarkable efficiency. Despite its deep system integration, DTrace is designed to have minimal impact on overall system performance. This efficiency makes it a feasible tool for use in live production environments, where maintaining system stability and performance is crucial. Its non-intrusive nature allows developers and system administrators to conduct thorough debugging and performance analysis without the worry of significantly slowing down or disrupting the normal operation of the system. Cross-Platform Compatibility Originally developed for Solaris, DTrace has evolved into a cross-platform tool, with adaptations available for MacOS, Windows, and various . Each platform presents its own set of features and limitations. For instance, while DTrace is a native component in Solaris and MacOS, its implementation in Linux often requires a specialized build due to kernel support and licensing considerations. Linux distributions Compatibility Challenges on MacOS On MacOS, DTrace's functionality intersects with System Integrity Protection (SIP), a security feature designed to prevent potentially harmful actions. To utilize DTrace effectively, users may need to disable SIP, which should be done with caution. This process involves booting into recovery mode and executing specific commands, a step that highlights the need for a careful approach when working with such powerful system-level tools. We can disable SIP using the command: csrutil disable We can optionally use a more refined approach of enabling SIP without dtrace using the following command: csrutil enable --without dtrace Be extra careful when issuing these commands and when working on machines where dtrace is enabled. Back up your data properly! Customizability and Flexibility of DTrace A key feature that sets DTrace apart in the realm of system monitoring tools is its highly customizable nature. DTrace employs a scripting language that bears similarity to C syntax, offering users the ability to craft detailed and specific diagnostic scripts. This scripting capability allows for the creation of custom probes that can be fine-tuned to target particular aspects of system behavior, providing precise and relevant data. Adaptability to Various Scenarios The flexibility of DTrace's scripting language means it can adapt to a multitude of debugging scenarios. Whether it's tracking down memory leaks, analyzing CPU usage, or monitoring I/O operations, DTrace can be configured to provide insights tailored to the specific needs of the task. This adaptability makes it an invaluable tool for both developers and system administrators who require a dynamic approach to problem-solving. Examples of Customizable Probes Users can define probes to monitor specific system events, track the behavior of certain processes, or gather data on system resource usage. This level of customization ensures that DTrace can be an effective tool in a variety of contexts, from routine maintenance to complex troubleshooting tasks. Following in a simple hello world dtrace probe: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p 9999 The kernel is instrumented with hooks that match various callbacks. dtrace connects to these hooks and can perform interesting tasks when these hooks are triggered. They have a naming convention, specially: . In this case the provider is a system call in both cases. We have no module, so we can leave that part blank between the colon ( ) symbols. We grab a write operation and entries. When an application writes or tries to send a packet, the following code event will trigger. provider:module:function:name : sendto These things happen frequently, which is why we restrict the process ID to the specific target with . This means the code will only trigger for the PID passed to us in the command line. The rest of the code should be simple for anyone with basic C experience; it's a printf that would list the processes and the data passed. pid == $target Real-World Applications of DTrace DTrace's diverse capabilities extend far beyond theoretical use, playing a pivotal role in resolving real-world system complexities. Its ability to provide deep insights into system operations makes it an indispensable tool in a variety of practical applications. To get a sense of how dtrace can be used, we can use the command whose output on my Mac is below: man -k dtrace bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace
Tcl_CommandTraceInfo(3tcl), Tcl_TraceCommand(3tcl), Tcl_UntraceCommand(3tcl) - monitor renames and deletes of a command
bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace There's a lot here; we don't need to read everything. The point is that when you run into a problem, you can just search through this list and find a tool dedicated to debugging that problem. Let’s say you're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? rwbypid.d can help you with that. It can generate a list of processes and the number of calls they have for read/write based on the process ID, as seen in the following screenshot: We can use this information to better understand IO issues in our code or even in 3rd party applications/libraries. is another tool that helps us track IO operations but with more details: iosnoop In diagnosing elusive system issues, DTrace shines by enabling detailed observation of system calls, file operations, and network activities. For instance, it can be used to uncover the root cause of unexpected system behaviors or to trace the origin of security breaches, offering a level of detail that is often unattainable with other debugging tools. Performance optimization is the main area where DTrace demonstrates its strengths. It allows administrators and developers to pinpoint performance bottlenecks, whether they lie in application code, system calls, or hardware interactions. By providing real-time data on resource usage, DTrace helps in fine-tuning systems for optimal performance. Final Words In conclusion, DTrace stands as a powerful and versatile tool in the realm of system monitoring and debugging. We've explored its broad capabilities, from in-depth system analysis to individual process tracing and its remarkable performance efficiency that allows for its use in live environments. Its cross-platform compatibility, coupled with the challenges and solutions specific to MacOS, highlights its widespread applicability. The customizability through scripting provides unmatched flexibility, adapting to a myriad of diagnostic needs. Real-world applications of DTrace in diagnosing system issues and optimizing performance underscore its practical value. DTrace's comprehensive toolkit offers an unparalleled window into the inner workings of systems, making it an invaluable asset for system administrators and developers alike. Whether it's for routine troubleshooting or complex performance tuning, DTrace provides insights and solutions that are essential in the modern computing landscape. Also published . here

Advanced Debugging Techniques: Revisiting DTrace for Troubleshooting

Twitter(now X) indubitably has the largest crypto community on the internet. Picture this: millions of users and a vast market for crypto investors to tap into. But as Elon Musk once admitted, is currently plagued by a bot problem that needs a quick fix. crypto Twitter A found that many of the biggest crypto profiles have 100,000s of fake ‘bot’ followers inflating their communities. These same fake profiles are also used to promote projects with artificial and low-quality engagement on social posts. Elon Musk’s attempts to prevent this since he acquired the company have had limited results, and what is more, Twitter/X is predicted to lose users over the next two years - almost 10% of its active user base. study earlier this year 30 million a new social-fi company, is amping up efforts to improve the level of social engagement on Twitter posts and help fight against the countless fake profiles across the platform. I had the opportunity to sit down with 8an, co-founder of /Reach, for a discussion about /Reach’s mission to help fix Crypto Twitter. /Reach, Q1: Can you tell us about yourself and your route to web3? My background started in consumer goods. I started my career at Rocket Internet as a Global Venture Developer, where we would launch day-one revenue-generating businesses in Europe and Emerging markets within 90 days from conception. After having gone the entrepreneurial route, namely as a founding team member of Numan (which grew to be the largest digital men’s health clinic in Europe), I founded Particle, a real-world assets on-chain fractionalization protocol focused on fine art. We famously tokenised Banksy’s $13M Love is in the Air in January 2022. I’ve been passively investing in Bitcoin since 2014, but I became more active in 2017 and then more ingrained in the community in 2021, like a lot of people. Q2: When Elon Musk bought Twitter a year ago, he promised to fix its bot problem once and for all. Do you think it is a herculean task to beat Twitter bots? It’s definitely not easy, but as with everything, recognising that there is a problem is a big first step. I think it's very much doable, especially with Musk's approach, which is to essentially make it economically deterrent for bots to exist - a similar approach to what /Reach is doing to those seeking to abuse or game the system. The goal, in the long run, is to make it unviable for a bot or exploiter ecosystem to exist. Q3: You have rightly pointed out that Crypto Twitter has a massive bot problem. Can you tell us how serious this problem is? Reportedly, up to 10% of X’s user base are bots. How deep this problem goes, we’ll only know once Musk reveals the extent of the issue as he has promised. What I can say is that within Crypto Twitter, it definitely is something everyone--from project owners, community leaders, artists, and individuals--sees as detrimental to their efforts. Q4: What can you tell us about the founding of /Reach, and how do you intend to tackle this problem? Attention and reach are the greatest challenges for brands, community builders, artists, and projects to crack. The X algorithm is broken, and Musk’s Ads Revenue Sharing program has fallen short of properly remunerating accounts for their reach. The “pay influencers” model is broken too - pseudo/low-value engagement doesn't help bring true visibility to comms that need to be promoted – with unfair and opaque pricing structures. Lastly, in an effort to maintain integrity on their profiles, even a brand’s greatest champions are reluctant to show support. Many aren’t even aware of which pieces of content would be most impactful for them to share. To elaborate further: Modern online tribes and communities, from gaming servers to Web3 projects, have become advertising-resistant, disregarding messages even from Key Opinion Leaders (KOLs). Traditional advertising struggles to infiltrate these tight-knit digital circles authentically. Reputable Web2 brands, despite having considerable media budgets, have yet to effectively access these communities and tribes in a manner that feels genuine and is welcomed. The outsider perception of brands is a significant barrier. Acceptance within these communities hinges on genuine endorsement from active, core tribe members. We approach this problem by harnessing the very ethos of Web3 in having the participants in a network equally participate in its success. The strategies we employ include the promotion of content, products, and services within active, fervent communities across diverse platforms – Web3, gaming, sports, and gambling, among others – organically and unobtrusively; overcoming the ‘Outsider Stigma’: Navigating through the walls of advertising resistance by enabling promotions through trusted, native voices within each tribe or community; and delivering a win-win scenario, allowing communities to monetize their user base while brands authentically connect with fresh, engaged audiences. Q5: What role does Twitter play in keeping the crypto community active? Why is Twitter the platform with the most crypto users? Twitter is our open forum and our playground, it is extremely important in the crypto community. It’s supported, of course, by Discord, Telegram, and WeChat, but Twitter’s model of short, bite-size news-esque delivered content is perfect for the fast-paced nature of Crypto. Q6: What are some of the strategies employed by /Reach in combating Crypto’s Twitter bot problem? We won’t be able to fully solve X’s bot problem alone, but we will be able to make sure it does not affect the spend creators put through the /Reach protocol. The /Reach protocol incentivizes both quality engagement and volume of engagement –both necessary in the success of visibility and fostering high caliber conversations and reach. We’ve implemented purpose-built, AI-powered, dynamic algorithms to power this effort. It is this quality engagement involving real people with authentic opinions that is our greatest weapon against bots on Twitter. Q7: From the information I have, you seem to have collected together a group of very impressive backers. Can you tell us a little bit about them and how you work with them? All of them are users of the /Reach protocol throughout the beta phase. The product and its results spoke for itself and they wanted to get involved. Many of the big names we speak to regularly to get their advice and perspective within the realm of their expertise. Q8: What are the next steps for /Reach after your launch? Grow the /Reach audience. We have already signed two deals with major communities to start using the /Reach protocol and have a pipeline of over 20 conversations at varying degrees of the sales cycle. The goal is to grow to an audience of more than 1,000,000 users within three months of launch.

Fixing Crypto Twitter: Exclusive Interview With /Reach Co-founder 8an

Ever chatted with an AI and gotten a response so out there, it felt like it came from a parallel universe? LLMs (like all of us) occasionally have day-dreams, also known as "hallucinations". Stick with me, and I'll show you a quick and easy fix to prevent this from happening to the product you're working on. Different Types of Hallucinations: The Obvious Untruths: An AI just told you about a president who never existed. The LLM has made up events, people, and places that are as real as unicorns, . entertaining, but not exactly helpful when you're after the facts The Oversharer: In contrast to making things up, some LLM responses go overboard with details. Before you know it, a user gets access to information it shouldn't have. . It raises concerns about privacy and the ethical use of data The Gibberish Generator: And sometimes, it's like they just smash the keyboard and hope for the best, similar to a baby with alphabet soup. . Not what you want if this output is fed to another function It is the fastest growing open source LLMOps platform, and the only one built for full stack developers with first-class TypeScript Support. Just a quick word about Pezzo: Like our mission? . We're building for developers, by developers 🧙‍♂️ Check Pezzo out and give us a star Why Do Hallucinations Happen? The Desire to Please: LLMs hate letting us down. When faced with gaps in data, it may invent details to fulfill the user's request, prioritizing continuity over accuracy. Outdated Information: An LLM's knowledge can be outdated. In fact, it is only as current as its last update. This can lead to hiccups in their responses. Preventing Hallucinations With "Sanity Checks" Sanity Checks are our way of teaching AIs to pause and ponder before blurting out whatever comes to mind. It's like giving them an internal filter to weed out the wacky before it hits our screens. This is how you do it: Add to your prompt an example of a thinking process where you stop after every response and perform a check about that response. The subject doesn't matter, the LLM will follow the reasoning structure. You can copy and paste this example to your prompt: Question: Who lived longer, Albert Einstein or Isaac Newton?

Answer:
Are follow up questions needed here: Yes.
Follow up: How old was Albert Einstein when he died?
Intermediate answer: Albert Einstein was 76 years old when he died.
Follow up: How old was Isaac Newton when he died?
Intermediate answer: Isaac Newton was 84 years old when he died.
So the final answer is: Isaac Newton. The Evolution of Prompting This prompting technique is a natural evolution from Direct Prompting and Chain-Of-Thought Prompting (see pic below). Here's the cool part: Sanity Checks Prompting reveals the LLM's thought process, letting us peek into the "black box" as it breaks down complex queries into bite-size follow-ups. The LLM knows it's reached the destination when it can seamlessly transition from intermediate answers to a final, confident conclusion. are a fixable glitch, not a dead end, and you're now ready to tackle them head-on. AI hallucinations Take these tools, push the AI to its limits, and make it work for you. If there's a specific bug you can't iron out with your AI, let's hear it below. Your challenge might just be the star of our next troubleshooting guide. Go on, give it a whirl, and report back 🚀 This article is inspired by the paper Measuring and Narrowing the Compositionality Gap in Language Models

A Secret Technique To Sidestepping LLM Hallucinations 

To say that artificial intelligence (AI) has been disrupting industries left and right is a mere understatement. From automation, and generative AI, to Natural Language Processing (NLP), these tech breakthroughs are continuously revolutionizing a myriad of organizations and domains over the last several years. With its lightning speed of global adoption and utilization, AI shows no signs of slowing down anytime soon. For legacy industries with traditional business models, embracing AI can still be a challenge. For one, there are some organizational facets that are simply irreplaceable by machines. While processes and operations are exponentially optimized, certain business aspects still call for critical thinking, out-of-the-box creativity, natural discernment, and human connection. The Learning & Development (L&D) space is no exception to this disruption. In fact, progressive business leaders and HR teams welcome these advancements and are looking forward to the vast opportunities brought about by AI. While of employees still prefer learning from an expert, L&D professionals are optimistic about the benefits of strategically incorporating AI into their initiatives and training programs. 87% Now, the question is: Can all L&D activities be led by AI? This article will dive into the L&D categories that can seamlessly adopt and utilize AI technologies, the segments that are irreplaceable by machines, and how to find the right balance in between. To decipher how AI can be implemented in the L&D domain, it’s paramount to understand the two main categories in the L&D space: and . on-demand/self-paced expert-led programs On-Demand/Self-Paced L&D Programs On-demand training empowers trainees to learn at their own pace via training platforms such as LinkedIn Learning. These programs grant them the flexibility to choose when and which courses to take, aligning with their needs and convenience. On-demand courses consist of diverse content elements, such as videos, class recordings, PDFs, and presentations, which each learner can navigate through at their individual speed. What’s more, the training platform effortlessly saves their progress, allowing them to resume from where they left off in their own time. This stands in contrast to traditional training methods, such as classroom settings with instructors who dictate the course's tempo and content in each session. Self-paced training provides learners with significantly more independence, enabling them to access pertinent materials when required and fostering greater engagement as they dictate their learning speed and priorities. Can it be led by AI? . Definitely Given its structure and nature, AI will be revolutionary in this segment – from pinpointing your specific learning objectives, discovering tailored content, and saving valuable time by avoiding irrelevant material. This technology promises a more interactive and adaptive learning experience, catering to your individual learning style and providing precisely targeted exercises. Moreover, AI is poised to enhance the on-demand, self-paced learning experience by introducing follow-up support and ensuring the practical implementation of acquired knowledge. Expert-Led L&D Programs Expert-Led L&D Programs are facilitated by industry experts, whether it's in an online or classroom setting. This model can take various forms, such as lectures, interactive workshops for hands-on skill practice, group/team coaching or 1:1 coaching/mentoring. Regardless of the delivery method, it serves as an excellent choice for organizations that require specialized training on intricate subjects, prioritize interactive and practical learning, value face-to-face engagement, and can allocate time for learners to fully immerse themselves in extended training sessions. Can it be led by AI? . Limitedly Yes, AI can streamline some of processes involved in expert led-learning, and automate some technical elements. Use cases include leveraging generative AI for better assessment of the learning targets, better matching of an expert to a cohort or individual, tailoring bite size learning to support the expert-led sessions, or automated follow-ups to support continuous learning. In doing so, it can uncover valuable insights and offer personalized recommendations – all with increased scalability and efficiency. Not in the foreseeable future. Expert-led L&D programs bank on direct person-to-person interactions and nuanced human experiences that machines currently don’t provide. Think of the 2013 movie “Her” – it’s the utopia of AI where an OS and a human are almost indistinguishable. However, it’s still science fiction, and we still have a long way to go before that becomes a seamless reality. But will machines completely replace human experts? At the end of the day, AI still lacks the genuine qualities of a human expert in your field of interest, and trusting a machine for learning and skill development can be dubious. In the short term, this trust may not yield significant benefits; over the long term, it could potentially show promise. But There Are Things AI Can’t Replace While AI can definitely optimize and automate low-quality L&D elements, there are things it can’t replace – primarily, the human connection. The unique quality of a human connection is the ability to foster trust and build rapport. When we interact with another person, we engage not only in words and actions but also in the exchange of emotions, empathy, and shared experiences. This multifaceted interaction creates a profound sense of trust that forms the foundation of many collaborations, including learning. On the other hand, AI operates within the confines of algorithms and data analysis. It can process vast amounts of information and make predictions or recommendations based on patterns and data trends. However, it lacks the emotional intelligence and intuition that humans naturally possess. AI (still) cannot read between the lines, pick up on subtle nuances, or understand the intricate web of human emotions that influence our decisions and actions. These factors are critical in learning and development – from motivating individuals to learn, and encouraging employees to advance their careers, to building a sound and healthy working relationship. The Key is Finding the Balance We are at a stage when no one can totally disregard AI because of its limitations, or completely replace all human-led L&D activities with machines. At the end of the day, it’s all about navigating the spot where AI can fill the void, bridge the gap, and streamline traditional training methods. Finding the right balance between human and machine is the key ingredient for you to thrive in today’s competitive world. Consider the challenge of identifying what aspects of one's work or skill set need enhancement. For some individuals, self-assessment may come naturally; they can readily discern their strengths and weaknesses. However, this self-awareness is not universal, and many people may struggle to pinpoint areas in which they need to develop to achieve desired outcomes. This is where AI steps in as a valuable ally. AI effortlessly analyzes vast datasets and identifies patterns, trends, and correlations that might be fallible to humans. In L&D activities, AI can serve as a guide, helping individuals recognize areas of improvement based on data-driven insights. Microsoft called their AI “co-pilot” for a reason. Think of AI as a co-pilot in an aircraft. The human pilot remains in control, making critical decisions and ensuring the overall success of the flight. However, the co-pilot provides support by managing side tasks, providing valuable information, and alerting the pilot to potential issues. This collaboration enhances the overall efficiency and safety of the journey. Conclusion The key takeaway is treating AI as a tool that can assist you; not necessarily replacing humans, but refreshing obsolete methods for efficiency and cost-effectiveness. Those organizations that will embrace, adapt, and utilize these inevitable advancements will find themselves earning an upper hand in today’s cutthroat digital age, in which those who still cling to old practices will be left behind. At the same time, an overreliance on machines is an equally dangerous territory that one has to avoid. Human intuition, empathy, and adaptability remain indispensable assets that cannot be replicated by AI. While incorporating AI into L&D strategies, it is essential to preserve the uniquely human qualities that contribute to innovation and resilience.

Can All L&D Activities Be Led By AI?

Presentations can be daunting to put together. I know what you are thinking, you've got a pile of information which all seems quite relevant which you need to simmer down into bite-size pieces to attract potential investors, right? Well, fear not, there are many different strategies that you can use to is not dull but rather engaging and timely so investors can go away with a clear understanding of the overall objective and message of the presentation. make sure your presentation Introducing to you the 10/20/30 Rule. A successful venture capitalist, , came up with the 10/20/30 Rule of PowerPoint. The suggested rule offers an excellent structure for presentations aiming to keep your audience's attention span from straying and is designed to keep your presentation compact, digestible and understandable. Guy Kawasaki Image Source: 180 Degrees Consulting The structure of the rule means your presentation should consist of no more than ten slides, shouldn't last longer than twenty minutes, and should contain a font size no smaller than thirty points. The great thing about the rule is that it is relevant to any business or type of presentation, be it an investor pitch or employee training, making it applicable and resourceful to all. It's quite easy to overfill the presentation with facts and text, which is why using visual data representation is a great way to present your findings. Keeping your presentation full of impressive visuals can help keep your potential investors engaged. The first part of the rule is to use a maximum of 10 slides per presentation. Make use of pre-made slides that have the structure already laid out to adhere to. The example below is based on a aimed at potential investors; however, the rule applies to all industries by customising the slides to meet your needs better. business presentation Image Source: poweredtemplate.com - in the first slide, identify the area of concern you will focus on in your presentation. The amount you’re seeking to raise should be discussed either during this slide or in your overview statement. Problem Focus on covering the following points: What’s the problem you’re solving? Explain the issue in detail, and why is it important to act now Why has no one solved it before? - once you have identified your problem, the key to any is to offer a solution to the problem. Ensure to speak facts and data. Explain why you’re the person/company to offer the solution, and how is your approach different from the others who’ve tried. Solution good presentation - now take a look at the business as a whole and how will you implement the solution considering the . Investors are primarily interested in making a profit (for some, it’s the one and only goal), hence, consider focusing on explaining how you’re going to return on their investments. Business model business strategy - discuss any new or existing software/hardware you will use to reach your goal. If you're a SaaS startup seeking a seed round, use this slide to show mockups of your technology/software. Technology - if the presentation is based on a product launch, for example, discuss how you will be getting the product to your audience. Reflect on the marketing budget and strategies you’re planning on implementing to drive traffic and awareness to the business (e.g. Paid Ads, SEO, Influencer Marketing, Content Marketing, etc). Marketing and Sales - outline what you're up against, who are your competitors? Explain how your model is different and how exactly are you going to beat the competition. Competition - gives everyone clear roles and collectively will help reach the end goal. Discuss the size of the team required, positions and hiring strategies. Team delegating tasks amongst a team - setting small goals to eventually lead up to the bigger target is a good way of approaching and managing the solution. Explain how you’re going to reach these steps. Projections and Milestones - timeline and forecast when tasks should be completed so that everyone is clear of the time frame to get the job done. Status and Timeline - talk about all the points you have discussed and what the next steps are to get the process started. Summary The second part of the rule is to stick to a 20-minute-long presentation. Keeping presentations short not only helps keeps your but also gives you more time at the end to answer questions or address any concerns your audience may have. With investor pitches, it’s definitely a good strategy to leave some time to respond to questions. audience engaged Running over time could potentially be dangerous, leading to an uninformed disengaged audience. The last part of the rule for a is to keep the font size at 30 or relatively close to it. It may seem like 30 points is big, but in retrospect, using smaller fonts can make your presentation harder to see - not something you’d want to happen during your pitch. winning presentation The use of smaller text can make the slide look text-heavy. The method of using a more prominent and clear font shows your knowledge and assurance in the information you are sharing. Most times, presenters squeeze information onto a single slide because they are not fully familiar with the topic. As you are the one who has to present, do the talking and persuade the investors to give you money, you have to know your stuff by heart, ensuring a flawless experience.

Winning Formulas: Obeying To The 10/20/30 Rule In Investor Pitching Presentations

Keeping up with the latest developments in Artificial Intelligence (AI) can be a challenging task given the rapid pace of innovation in this field. Newsletters can be an invaluable resource for staying up to date, providing a curated selection of the latest news, research, and insights directly to your inbox. Here are the top 20 AI newsletters you should consider subscribing to in 2023: : PromptPal is the platform where to share and discover the best AI prompts for ChatGPT, Midjourney, Bard, Bing, and more. Their newsletter is filled with the best free prompts, the best AI tools to use, the latest AI news, the best new Midjourney images and more. It’s our favourite AI newsletter. PromptPal : Provides a weekly collection of the latest in AI/ML research, tools, and projects. Ideal for professionals interested in a bite-size minimalistic format that covers the technical side of AI. Data Machina : Written by Jeremy Kahn of Fortune Magazine, this newsletter provides a long-form look into the AI industry and the implications of tools like ChatGPT. Fortune: Eye on AI : Provides a critical lens into the world of AI, covering the second-order implications of new artificial intelligence technologies and the ethical dilemmas that we face. The Road to AI We Can Trust : Perfect for a quick dose of AI news, The Neuron provides short reads and humorous updates in the AI world. The Neuron : A weekly newsletter written by Alex McFarland, AI Disruption highlights the most significant AI developments each week. AI Disruption : A daily newsletter that offers brief, punchy insights into new AI tools, how-to guides, and creative AI use cases. Perfect for anyone looking to understand the most interesting tweets and product releases in AI, regardless of technical skill level. Ben’s Bites : Provides AI news and commentary from the perspective of a builder in the space. This a fantastic resource if you are interested in the perspective of someone actively building AI tools and technologies. Machine Learnings : A weekly email that provides a deeper dive into the ethical and technical aspects of AI, perfect for those that are ready to dig further into the world of AI. AI Weekly Digest : Translates academic research in the field of AI, machine learning, and data science into useful insights for everyday people. One Useful Thing : A must-read monthly newsletter for anyone looking to gain a more comprehensive grasp of the AI industry, politics, and ethics. Guide to AI : Provides an in-depth look into cutting-edge AI research and explains why it matters in an easy-to-understand manner. Written by Jack Clark, the former Policy Director at OpenAI. Import AI : Offers short, organized, and very visual weekly roundups of AI news. Ideal for anyone with only a few minutes each week to read about AI news. Its emphasis is on providing quick and succinct updates without personal takes, summaries, or endless scrolling. AI Tidbits : Weekly AI summaries written by graduate students in the field, providing recaps of the biggest developments of the week. The emails focus on research and business-related AI news, while also providing great commentary on public policy and technical implications. Last Week in AI : A new newsletter delivering the latest AI tools and resources for the startup and builder audience. It offers simple, weekly emails that can be read in less than 5 minutes. Meta Minded : Provides daily updates on what’s happening in AI and robotics, offering varying opinions from a wide array of people in the industry. It also features a curated news section on the latest in Artificial Intelligence. Inside AI Newsletter : More focused on insiders and established professionals in the field of AI, Data Elixir shares information relevant to industry experts on the latest developments in artificial intelligence and machine learning. Expect to find new tools useful for AI engineers and job postings here. Data Elixir : Authored by Daniel Bourke, this monthly newsletter covers the most interesting news over the last month in AI. Each newsletter takes about 10 to 15 minutes to read, making it a good option for readers who don’t want a weekly/daily update on the industry. Zero to Mastery Newsletter : A highly respected newsletter by the MIT Technology Review that covers the latest news and trends in AI. It includes updates on machine learning, neural networks, and data science in general. The Algorithm : Curated by Rohin Shah, this weekly newsletter provides updates on technical and philosophical progress related to AI alignment, a field of study that focuses on ensuring that AI systems will be beneficial to humans. AI Alignment Newsletter : This popular TikTok channel also sends out newsletters featuring easy-to-follow how-to guides on AI image and text generation, along with fun examples of AI use cases. Tech N Trendz (AI Tools) There you have it! These are the top 20 AI newsletters you should consider subscribing to in 2023. They offer a wide range of perspectives and cover a variety of topics, from the latest AI tools and research to ethical considerations and industry trends. Happy reading!

21 AI Newsletters You Should Subscribe to in 2023

21 boletines informativos de IA a los que debería suscribirse en 2023

21 newsletters sur l'IA auxquelles vous devriez vous abonner en 2023

21 AI न्यूज़लेटर्स आपको 2023 में सब्सक्राइब करने चाहिए

2023 年に購読すべき 21 の AI ニュースレター

21 boletins informativos de IA que você deve assinar em 2023

21 bản tin AI bạn nên đăng ký vào năm 2023

2023 年你应该订阅的 21 份 AI 时事通讯

Often when debugging, we need to step outside of the comforting embrace of the IDE to reproduce or track an issue. In this series, I’d like to go over some tools you might find useful for these cases. I’ll try to limit myself to tools that are 100% debugging tools and not those that are useful for development testing. E.g. tools like or are remarkably useful. You can/should use them while debugging. But you probably used them while building and testing the feature. So you would already be familiar with them and should have some sense of what they do. I want to focus on tools that you would mostly reach for when debugging. In that sense, tools like etc. also make no sense here. curl jq SDKMan I also want to avoid tools like database tools. They are remarkably useful when debugging, but again, you’d probably use them during development, too. They’re also very vendor specific, so it’s a vast subject that’s too broad to cover here. The tools I'll cover in this series encompass the following categories: System monitoring tools - as is the case with strace and dtrace, which we'll discuss today Network monitors - also fits in the above bracket but warrants a category of its own VM/Runtime monitoring - e.g. tools that let us inspect the JVM, etc. Profilers and memory monitors In this first post I'd like to discuss two heavyweight champions: dtrace and strace. If you're a Java developer or a Windows user, there's a good chance you never heard of these tools. You might have inadvertently used one of them since so many tools were built on top of them, but that's probably not the case. Both tools will let you debug anything without the source code. You could detect problems and gain a level of understanding you never imagined. Dtrace Back in 2004, I first heard about DTrace while working at Sun Microsystems. It became all the rage in the hallways as it was an innovation that Sun Microsystems was promoting. Dtrace was ported later into MacOS X (it originated on Solaris). Today there are ports on Windows and Linux as well. Dtrace is a powerful low-level dynamic tracing framework. But that's just another superlative and if you never used such a tool and don't have a background in systems programming, you might be feeling a bit confused: what the hell does it do? It lets you "see" everything. Want to know which files a process has opened? OK Want to know who invokes a kernel API and get a stack trace to the caller? OK Want to know why a process dies? OK Want to know how much CPU Time is spent on an operation? OK You might think that dtrace is one of those tools that will destroy your CPU completely... But here's the killer feature: it's fast enough to run in production with minimal to no performance impact! It was revolutionary when it was launched almost two decades ago and it's still the case to this day! Running Dtrace Before we begin, a word of warning. Save your data! This tool can easily crash your machine and enabling it requires disabling important security facilities on MacOS. This is a risky "low level" system service and should be treated as such... On a Mac dtrace conflicts with "System Integrity Protection" which is a security feature that blocks some interactions between processes (among other things). Under normal circumstances, this would be great. But if you want to run dtrace this would be a problem. The solution is booting to recovery mode on Intel Macs; this means holding the keys on boot. On an ARM mac just long-press the power button. Command-R Then, in a recovery mode terminal, issue the command: . csrutil disable Then upon reboot, dtrace should work as expected. Basic Usage As mentioned before, dtrace is a very powerful tool. There are whole books written about it. It has its own programming language based on C syntax that you can use to build elaborate logic. E.g. the following command will log some information from the given callbacks: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p [PID] The code snippet passed to the dtrace command listens to the sendto callback on the target process ID. Then prints out the information to the console, e.g.: (pid) text If this seems like a bit too much and too hard to get started with... You're 100% right. It's a powerful tool when you need it. But for most of our day-to-day usage, it's just too powerful. What we want is to know a bit of basic stuff and this is just too much! Simple Usage As luck would have it, we have a simple solution: man -k dtrace This prints out a list of tools that's worth reading just to get a sense of how extensive this thing is. Here are a few interesting lines of output from that command: bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks It's worth your time going over this list to realize what you can truly do here. Examples You're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? Just run: sudo rwbypid.d It will print out the reads/writes to the disk: PID CMD                       DIR    COUNT
  2957 wordexp-helper              W        1
  2959 wc                          W        1
  2961 grep                        W        1

... snipped for clarity ...

   637 firefox                     R     6937
   637 firefox                     W    15325
   343 sentineld                   W   100287 The security software is really holding performance down... You can also use to get more specific results on the number of bytes written/distribution. bitesize.d That's pretty high level though. What if you want specifics: file name, process name, etc? sudo iosnoop -a Prints out output that includes pretty much everything you would need: STRTIME              DEVICE  MAJ MIN   UID   PID D      BLOCK     SIZE                     PATHNAME ARGS
2022 Jun 30 12:16:56 ??        1  17   501  1111 W  150777072     4096 ??/idb/3166453069wcaw.sqlite-wal firefox\0
2022 Jun 30 12:16:56 ??        1  17   501   661 W  150777175   487424  ??/index-dir/the-real-index Slack Helper\0
2022 Jun 30 12:16:57 ??        1  17   499   342 W  150777294     4096 ??/persistent/.dat.nosync0156.ztvXap sentineld\0 I can see the process id and how many bytes it wrote to the specific file! Let's say your program spans processes and you want to see what's going on. E.g. I run a source code build in a server I built: sudo errinfo Lets me detect the error returned from system calls to and the command that originally triggered that: EXEC          SYSCALL  ERR  DESC
    WindowServer workq_kernreturn   -2 
    WindowServer workq_kernreturn   -2 
   SentinelAgent workq_kernreturn   -2 
   SentinelAgent workq_kernreturn   -2 
          Signal           Helper    0 
          Google           Chrome    0 
           Brave          Browser    0 
          Google           Chrome    0 These are just the tip of the iceberg. I suggest checking out this old from Oracle or . Disclaimer: I didn't read the book... dtrace tutorial the book strace Amusingly enough, the strace tool also originated at Sun Microsystems in this case in the 90s. This isn't surprising though as the list of technologies originating from Sun Microsystems is absolutely mind-numbing. Strace is much simpler than dtrace in both usage and capabilities. For better and for worse. Since dtrace requires deep OS support, it never became an official feature of common Linux distributions and as a result, on Linux people use strace instead of dtrace. They aren't exactly interchangeable, though. Strace is enabled thanks to the kernel feature known as ptrace. Since ptrace is already in Linux, we don't need to add additional kernel code or modules. Typically, dtrace requires deeper kernel support, to get around licensing issues on Linux it's in a separate loadable module but that still presents some challenges. Working with strace is akin to printing a log entry every time we make a kernel call. This creates very verbose logging for every command you execute. As a result, you can follow what's going on under the hood of a running process. really Running Strace Nowadays, strace is commonly used in Linux, it's my favorite system diagnostic tool on that platform. It's remarkably convenient to work with since we can run it with no special privileges. Notice that, unlike dtrace, you should keep strace away from production environments (unless the code is segregated). It carries a significant performance overhead and would bring a production system down. The most basic usage of strace is just passing the command line to it: strace java -classpath. PrimeMain The output of strace for this is pretty long, lets go over a few of the lines: execve("/home/ec2-user/jdk1.8.0_45/bin/java", ["java", "-classpath.", "PrimeMain"], 0x7fffd689ec20 /* 23 vars */) = 0
brk(NULL)                               = 0xb85000
mmap(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7f0294272000
readlink("/proc/self/exe", "/home/ec2-user/jdk1.8.0_45/bin/j"..., 4096) = 35
access("/etc/ld.so.preload", R_OK)      = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
stat("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64", 0x7fff37af09a0) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
stat("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls", 0x7fff37af09a0) = -1 ENOENT (No such file or directory) Every one of these lines is a Linux system call. We can google each one of them to get a sense of what's going on. Here's a simple example: open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory) Java tries to load the library from the directory using a system open call to load the file. The exit code of the system call is , which means that the file isn't there. Under normal circumstances, we should get back a file descriptor value from this API. Looking in the directory, it seems the directory is missing. I'm guessing that this is because of a missing JCE installation. This is probably OK but might have been interesting in some cases. pthread tls -1 tls Obviously, the amount of output is overwhelming sometimes. We usually just want to see things like "which file was opened" and "what's going on with our network calls". We can easily accomplish that by only looking at specific system calls using the argument. -e strace -e open java -classpath . PrimeMain Will only show the open system calls: open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
open("/etc/ld.so.cache", O_RDONLY|O_CLOEXEC) = 3
open("/lib64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = 3
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/libjli.so", O_RDONLY|O_CLOEXEC) = 3
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/libdl.so.2", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory) There are many system calls you can learn and use to track many behaviors such as this: connect, write, etc. This is only the tip of the iceberg of what you can do using strace. Julia Evans wrote some of the most . If you want to learn more about it, there's probably no better place (also check out her other stuff... Amazing resources!). exhaustive and entertaining posts on strace Strace and Java As you saw before, strace works great with the JVM. Since strace predates Java and is a very low-level tool, it has no awareness of the JVM. The JVM works like most other platforms and invokes system calls which you can use to debug its behavior. However, because of its unique approach to some problems, some aspects might not be as visible with strace. A good example is allocations. System tools use malloc, which maps to kernel allocation logic, but Java takes a different route. It manages its own memory for efficiency and easier garbage collection logic. As a result, some aspects of memory allocation will be hidden from strace output. This can be a blessing in disguise, as the output can be overwhelming sometimes. At the time of this writing, threading works well with strace. But this might not be the case moving forward as might change the one-to-one mapping between Java threads and system threads. This might make strace output harder to pinpoint in heavily threaded applications. project Loom Finally There's an alphabet soup of "*trace" utilities in various forms that keep borrowing ideas from one another. It's a significant challenge to keep up with all of that noise. There are just too many great tools to cover, I would like to discuss btrace in a future installment though. It's very similar to dtrace but also very JVM specific so probably worth a different post. The tools I discussed today take different approaches to solve similar problems: how do we understand what a binary application "really" does? Security researchers and hackers use these tools to understand your program. They don't need the code and don't need a disassembly to see what you're actually doing. You can also use these tools to understand the impact of your actions. We often invoke an API and let things end there. But the devil is in the details and those details can carry a heavy toll. As a Java developer, I rarely think about signal delivery, process management or such low-level staples. But I do spend time looking at these things since they ultimately impact the stability and performance of my application. Also Published Here

Using External Debugging Tools dtrace and strace

We all know that tech is a pretty good career money-wise. Still, you can do yourself a disservice by sticking to lower pay than a company might offer. Even a tiny difference in pay becomes an impressive amount when you multiply it by 12 months in a year and all the years you’ll be working. Let’s ensure that you don’t miss out on financial opportunities. 1. Talk about pay with your peers The way talking about money is perceived in the culture. I grew up in Poland, and talking about my income felt like boasting to me for a long time. You could see this being the case if a well-paid specialist were to talk about their income to people who are stuck in minimum wage jobs. On the other hand, for people who work in similar jobs, the possible outcomes are: There is a minimal difference in compensation. Someone—maybe you—might realize that they are underpaid. There are a few reasons this could happen: you lack some technical skill that has a significant impact on pay you lack negotiating skills the company you work for has no budget to pay better And the people who learn that they are paid better? They lose nothing. Besides the cultural taboo, the only reason not to talk about money would be if you are more invested in the company’s bottom line than in your and your colleagues’ welfare. For example, this could be the case if your share of company ownership is more significant than your part of the total payroll, or you believe fully in your employer’s mission. 2. Evaluate the broader market regularly If you are like me, your focus on your career fluctuates a lot over the years. Sometimes I spend a lot of time learning new stuff engaging with tech communities, and sometimes I have months when I do nothing IT-related besides my day job. There are always changes in the job market: new technologies rising in popularity, new companies in the local market offering better conditions, or far away companies opening up for remote candidates. It’s a good idea to make an appointment with yourself to regularly check up on those changes. At a minimum, setting aside one afternoon a year for searching for job offers should make you aware of how things are going outside your current company. 3. Don’t count on the company’s initiative The company you work for is interested in maintaining their operations undisturbed. Maybe your manager’s evaluation depends on how many resign from their team, but this gives them only a small incentive to keep your salary in line with the market. This incentive will be even smaller if you convince them that you like your work and you are not searching for anything else at the moment. If the company has a policy of regular pay raises, it will most likely just match inflation, not the IT job market changes. If you are promoted to a different salary bracket that comes with a raise, it looks like an improvement, but it may be that you are placed at the bottom of this new bracket. 4. Prepare yourself for the negotiation In 2019 I bought myself as a birthday gift. It was the greatest gift I ever got, and it paid back for itself in no time. The book gives much practical advice—step-by-step guides for what to do before negotiating the salary. Dealing with a new company is a big part of it, but many things’ll be useful in a more general context, and one chapter is dedicated to promotions and pay raises. Fearless Salary Negotiation If you are on a budget, the book author generously offers it for . You can get his advice from his website, in the bite-size piece: . free how to write an email to your manager starting the conversation 5. Ask for it! Bringing up the topic of compensation with your manager can be stressful. The proper preparation should help you achieve your target and help you with the stress. You can look at it from another perspective—a reasonable manager should appreciate that you talk about your issue with the salary with them first, instead of going directly to the competition. And if they turn out to be unreasonable? It will mean that talking with other companies is a good idea. 6. Don’t be shy; let your colleagues know Once you secure your raise, you can let your colleagues know! The standard industry practice of jumping from job to job to get better compensation means that your experienced colleague could be replaced one day by somebody who knows nothing about your project, making you carry a more significant workload. Sharing your good news can inspire other team members to try doing the same, improving morale and making sure they stay longer in the project. In the end, this will make your job easier and more pleasant. Also published . here

6 Tips for Getting a Pay Raise in IT

Descriptive Analysis: Why Did These 500 Trending Startups Shut Down?

If you never heard the word refactoring before, the is a good place to start: code refactoring is the process of restructuring existing computer code without changing its external behavior. Refactoring is intended to improve the design, structure, and/or implementation of  software (its non-functional attributes) while preserving its functionality. wikipedia definition for it Even without knowing this definition, you are likely already applying refactoring every now and then, by renaming variables, extracting blocks of code to functions, extracting methods to classes, or modularizing classes that belong together to their own package. Refactor Cost Although refactoring is a positive thing, as its objective is to improve the quality of the target code, refactoring a piece of code is not always feasible. Refactoring has a significant cost because it demands developers working hours that won't translate directly into business impact: new functionalities, enabling new businesses, improvements on user experience. Instead, the impact of refactoring is indirect, and will only make a difference in the long run when developers will have an easier time changing that code, resulting in fewer bugs and faster deliveries. And even then, it might go under the radar if people are unable to correlate the success of the team to this well-laid foundation that was crafted in the past, so be sure to mention it to the team, and give credit where credit is due. The software we write (mostly) has the objective to enable a business, thus the time and money available for the crafting of this software are constrained by the available budget and market timings related to the business's products. The nature of this relation will create cycles where functionality is rapidly added and technical debt is accumulated, and cycles where technical debt must be paid for and functionality can't be added as quickly. Even with good and well-defined architecture, an efficient process of code-review in place, a coherent code style, and developers applying all the good coding practices, technical debt will still be created as features are added and evolved, but the debt will grow slower than it would if you didn't care about code quality. In the end, the good practices are there to slow down the increase of debt, so the happy cycle where functionality can be added blazing-fast lasts longer. All in all, refactoring involves a trade-off between velocity now and velocity in the long term. And it is our responsibility as developers to continuously evaluate this trade-off, communicate it to the stakeholders, and when necessary, advocate for large refactors. Martin Fowler has written an awesome article about this subject called , that in my opinion is a must-read for every software engineer and software engineering manager. Is High Quality Software Worth the Cost? Albeit having described accumulating and paying the technical debt as cycles, most of the time it won't happen as discretely as that. Stopping a whole product development team for an extensive period to work exclusively on code quality improvements is less than desirable, and for startups with smaller teams, it may not even be possible to do so. With time, as code quality decays, features take longer to be implemented, tasks are forever postponed because of technical limitations, bugs occur frequently, and fixing then seems beyond hope as more will just pop out. This can easily become a vicious cycle as the developers will only make the code even worse by adding more and more stuff to it and doing all kinds of quick fixes and workarounds ( as we say in Brazil) to get things done. Sometimes the only way out of this messy code swamp is to go for a total rewrite and start all over again in a clean sheet, but as we all know this is tough. gambiarra Not only is a big rewrite a lot of work, but it can also be a big hit to the team morale as the pressure to finish it fast and start shipping new features mounts. Maintaining Code Quality The ideal way to go about it is to never let things get so far out of hand to get to this doomed code state. Continuous refactoring will go a long way in ensuring the code is of good quality. Of course, that doesn't mean that you'll never have to engage in a huge refactor or a rewrite again, sometimes the business evolves in a way that requirements change or take gigantic proportions that weren't planned, and that's all fair and square. The book has a section about Software Entropy, and they make a beautiful analogy that bad code is like a building with broken windows. The feeling of abandonment will quickly spread among the developers, in a way that no one will try to repair it, or even take care to not damage it further. The remedy proposed by the authors is to not leave broken windows: fix issues as soon as you see them, or at least document the flaws and work on them later (later in this context means asap, not never). It is important to make a statement that that codebase is not abandoned. The Pragmatic Programmer Most of the refactoring is better done alongside product-driven tasks. You can utilize that you are working on a context anyway, and improve it by refactoring the bits that you found hard to understand, or that weren't flexible enough to accommodate your new use-case. The book makes a simple analogy to this attitude comparing it to a Boy Scout rule to always leave the camp cleaner than found. With enough people with this mindset, code quality can be improved over time. Clean Code Healthy Team, Healthy Code Keeping a healthy codebase is not a task to be tackled by an individual, refactoring messy chunks of code may be impossible without a supporting team. The improvements made by one person can be quickly dimmed by other developers going in the opposite direction. To be able to engage and keep developers comfortable in taking code quality seriously, it is important to have the whole team stand by it. The engineering team should be supporting each other by reviewing code, designing solutions together, pair programming, keeping track of technical debt, and backing each other up to keep the shipping business impact while making code refactors. Management has to be understanding by giving the development team the time and space necessary to keep delivering at a high quality and to shield the team from unnecessary feature demands. Product managers and designers have to be aware of technical complexity and be versatile to keep balance on what is an amazing product and what is simple to develop. Takeaways Code decays even if you are in a really good team with an excellent tech stack, routine refactoring (housekeeping) is part of the job. It's easier and safer to change good code, so refactoring is not a waste of time, it is an investment of it. Your non-technical colleagues may be unaware of the importance of refactoring. Explain to them how flexible and scalable the code became, praise your teammates when they do take on a refactoring quest. Constantly improving code quality is easier than rewriting it every now and then. Maintaining a healthy codebase is a collective challenge; keeping the team engaged in applying good practices; code reviews, refactoring, and so on, is as important (if not more) as the technical side of it. Beyond This Article Refactoring is a broad subject, so I'll be writing more about it. Next time, I'll go about hands-on refactoring techniques. The book from Martin Fowler and Kent Beck is one of the main references on refactoring. For a bitesize read, has awesome, easy-to-understand, and applicable tips and tricks. Refactoring Improving the Design of Existing Code Refactoring Guru Thanks for reading! Liked this content? I always announce new articles on my . Suggestions, feedback, and corrections are always welcome. Twitter Account First Published here

Why You Must Start Refactoring Your Codebase Today

*This story is a part of Hacker Noon's ** * How to get a job in tech initiative. The series is intended for tech professionals in any field to share their experience of building a career in tech and help bust the common myths beginner techies are facing. If you too would like to share your experience, . you can do so here What is your current position? Head of Developer Relations How long have you been working in tech? 22 Years What is your educational background? (It’s perfectly okay if you don’t have a formal one) University Degree Have you ever had a mentor? If yes, we’d like to know more about it. No What was the best piece of advice you got over the course of your career? Dependant, Independent, Interdependent. Everyone starts out dependent. You need others to help you do things. You eventually become independent and can function in your role without the need for outside assistance. This is where most people stop. There is another layer. To let people in, to play to your strengths, and allow others to do the same and become interdependent is how great teams are built. How relevant was self-learning to where you are today? It is everything. Knowledge given by others is not real knowledge because you didn’t have to suffer to attain it. Is it valuable, yes, but you don’t really understand something until you go through it yourself. Knowing what you do now, where do you think one should start learning if they want to work in your position one day? Start a side project and see it through to completion. Ideally to the point of making money, even if it’s just $1. You’ll learn more from that experience than anything you will learn at work. Most developers never finish anything. If you are one of the ones that do, you are in a very small group of people who are destined for success. What is the work-related achievement you’re the most proud of? I have hired hundreds of software developers and every single one has been hired on the basis of being a decent human being first and their technical ability second. This has never failed me and I went on to help build a brand well known for having a great culture. What do you think is the biggest myth about starting a career in tech? Programming is hard. I believe nothing in this life is that hard, though as things are often vast (programming is a huge subject) they can appear to be difficult and daunting. The truth is you just haven’t broken the subject down into small enough chunks. Break it down into bitesize chunks and it will appear very differently. On a less serious note: What do you listen to while working? A mixture between Moby - Wait for Me and my wife telling the kids off! Thanks so much for taking the time to tell us more about your career path. Any words of wisdom for aspiring techies? To quote Gary Vaynerchuk: “Stop focussing on dumb sh*t“ and get to work! I believe: Anyone can become a great programmer The best developers rarely get the job

“Everyone Starts Out Dependent”: David Roberts on Building a Career in Tech

Code Examples and Run Instructions are now located on GitHub I’m on a journey to enhance my resume with a wealth of new programming languages. For the past 12 years, I have specialized in PHP, though I’ve always dabbled with other languages. There is the obvious choice for specialists in the web industry: Javascript, but I have also dabbled with Python, Perl, Java, Swift, and Bash scripting. Recently, though I dearly love PHP, I’m just not seeing the career trajectory in the PHP space that I once saw and am taking other languages more seriously. Instead of dabbling with them, I’m moving into full-on hobby projects or courses to learn more about the languages at a deeper level. This series of articles is meant to serve two purposes. First, it’s going to be an example of how I learn new languages. This might not work for everyone, but it has worked well for me when I’ve needed to pick up a language in a new project over the past 12 years. Second, It’s going to serve as a bit of a tutorial or entry-level-learning opportunity for anyone interested in the languages I focus on. Learning The method for me is simple. I start with learning how to print to screen and store basic data (Hello World + Variables). I then move to more complicated data structures and control structures. Last, I learn their OOP (if applicable) syntax. At this point, though I’m far from an expert, I feel quite comfortable reading other peoples’ code or writing my own. This simple process is enough to at least understand the answers provided on Stack Overflow enough to implement them, which is all many of us do anyway. Tutorial I’m going to treat this series as a shortlist of lessons. My plan, though it may change as I get writing, is to create the following articles: Hello World - simple variable declarations, functions, and displaying data to the screen Everything Varies - Diving further into variable declarations with commonly used data types; how to declare them, how to use them, how to interact with them. Classes and Objects - The basics of OOP in each language (where applicable) Simple Project - A simple, fully functional project that uses OOP code, includes some basic libraries provided by/for the language and includes some form of user input/output. Languages Again, this is subject to change based on feedback but, based on my own desires, I plan to feature the following languages in article. every PHP (my current expertise, just to set a basis) Javascript TypeScript Python Java C/C++ Go Scala Rust These languages are chosen based on my own interests and also based on the popularity that I’ve seen through conversations with others in the industry or job postings that I’ve reviewed. I’m open to suggestions if there’s a critical language that many are interested in that I’ve missed. I’m not sure i’ll remove anything from the list, but i’m sure it’ll grow. Let’s Get Started! Hello World! The simplest, most commonly accepted way to start learning any new programming language but I’ve always found one major flaw with it. It’s useless. Yes, you learn to output data to the screen but not in a presentable manner. If anything this teaches you how you’ll debug your code as a beginner, before getting into the more common debug frameworks and methodologies. It’s just not a good starting point. A starting point should be basic, yes, but not so basic that it’s boring. I want to still be awake when I’ve completed my first task. So I usually add a very simple change that makes the exercise feel more valuable: string variable declarations. PHP So I had written out this long step by step instruction manual on how to construct this basic code block, different choices made along the way. I started simple and slowly complicated it…and I got bored editing it, so I can’t imagine how boring it would be to read. Lets do this. If you’re reading this, you’re interested in learning something. So lets break it down in bite-size chunks that won’t put you to sleep. Consider the following code: <?php

$firstName = "Jacob";
$lastName = "Landry";

echo hello($firstName, $lastName);

function hello(string $first, string $last): string
{
  return "Hello " . $firstName . " " . $lastName . ", how are you doing today?";
}

?> Output: Hello Jacob Landry, how are you doing today? We did three major things here: Variables Declaring variables in PHP is done with the $ symbol followed by a unique identifier. You can assign data to the variable with the equals sign. Variables are dynamically typed in PHP, which means you could overwrite any of those strings with an integer and no one would care (except for me, I care, don’t do that). Functions Declaring functions is done by using the word “function” followed by an identifier. Variables can be passed into a function the same way they are created. We do have the added opportunity (as of PHP 7) to declare a type-hint here. These type-hints are not required, and the code will work fine without them, but it gives us an opportunity to catch any issues or errors, and prevent ourselves or someone else from using this function incorrectly in the future. This is done by passing the type before the variable, in the function. . string $firstName You can return data from the function with the “return” directive. You can also provide a non-required type-hint for the return value as well (as of PHP 8) with :type at the end of the function, before starting the internal code. I do this above with but this is not required. Without this, you can return any data from the function without complaint. With it, you can still return anything you want but anything outside of a string will generate a warning. ): string Output Outputting data in PHP is done using “echo” or “print”, in most cases. Here I’ve chosen echo. This will print the string to the browser or terminal, depending on how the code is being executed. Javascript Javascript has a lot of similarities with PHP (lets be frank, most languages are remarkably similar at this basic level), so the same code looks as follows: var first_name = "Jacob"
var last_name = "Landry"

console.log(hello(first_name, last_name))

function hello(first_name, last_name) {
  return "Hello " + first_name + " " + last_name + ", how are you doing today?"
} Variables Variables can be declared with var, let, or const in Javascript. I’ve chosen var here for simplicity, maybe we can get into the other declarations in the future. You set data to the variable with the equals sign followed by the data. (Note: you can actually drop the “var” directive and it works just fine but…it feels dirty). Javascript is also dynamically typed so there is no type provided for the variables, and as far as I (or google) seem to know, there isn’t a way to provide hints. Functions Functions are declared with the word “function” followed by an identifier. You pass in variables by listing them inside the function input block. You return data with the “return” directive. Output Outputting data can be done in several ways in Javascript. Normally you would likely be editing the DOM on your website, but here we are simply outputting to the Javascript console in your browser. This is a simple way to debug early applications, though I wouldn’t bank on it as your skills and applications grow. TypeScript var first_name:string = "Jacob"
var last_name:string = "Landry"

console.log(hello(first_name, last_name))

function hello(first_name:string, last_name:string):string {
    return "Hello " + first_name + " " + last_name + ", how are you doing today?"
} Variables Declared by “var” followed by an identifier. A type can be provided with “:type” but is not required. The code will execute just fine without the type declared, or even if you break your type rules by assigning a number to a string. You will see some warnings, which are important to take note of, but the code will function fine. Functions Functions are declared with the word “function” and an identifier. You can add types to your variable inputs and as an output. Same as above, this won’t actually break anything but is nice to have for sanity reasons. Output Similar to Javascript, output can be achieved in a few ways but for this simple example console.log will output the data to the Javascript console in your browser. Note: Typescript is Javascript with Type-hinting. So the code is exactly the same but with stricter rules around data-types. Python first_name = "Jacob"
last_name = "Landry"

def hello(first_name, last_name):
  return "Hello " + first_name + " " + last_name + ", how are you doing today?"
}

print(hello(first_name, last_name)) Variables Similar to Javascript and Typescript, a variable can be declared simply by typing the identifier, followed by the = sign and the value. There is no static typing or declaration needed. Functions Functions are defined by the word “def” followed by the identifier and a colon. Everything inside the function must appear after the colon and indented. I did find that I could not call “hello” before it was defined, which is why I had to change the order of the code here. Clearly this would not be an issue in an OOP approach, but for something like this it was worth noting. Data is returned from the function with the “return” directive. Output Data can be output with print() and strings are concatenated the same as with Javascript with the “+” character. Java Note, this is where things start to get a little more defined. Java is a statically typed language and everything must be executed from within a class. This filename is helloworld.java and when you run it, it automatically executes the main method. public class HelloWorld {

	public static void main(String[] args) {
		String first_name = "Jacob";
		String last_name = "Landry";

		System.out.println(hello(first_name, last_name));
	}

	public static String hello(String first_name, String last_name) {
		return "Hello " + first_name + " " + last_name + ", how are you doing today?";
	}

} Variables Java is a static typed language, which means the variable types must be defined. Once they are defined, they cannot change. So in this example I have defined the class’ first_name variable as a String. Now, any time I assign a value to this variable it must be a string or the program will not compile and run. Variables are defined by the type followed by the identifier, then the = and value. Functions Functions are defined by their level of access (public, private, protected), then their return type (String), followed by the identifier. In this case I have also opted to make the function “static” which means it can be used without a created object. This just made the example easier to work with so we didn’t have to create a new class/object to run the function. Also, you’ll notice that you have to define the variable types for input as well. If you try to pass anything other than a string to this function the program will not compile and run. Data is returned from the function with the “return” directive. Output Output can be done in many ways but for this example we simply output to the console with System.out.println. Strings are concatenated with “+”, similar to other languages. C/C++ #include <iostream>
#include <string>

using namespace std;

string hello(string first_name, string last_name)
{
    return "Hello " + first_name + " " + last_name + ", how are you doing today?";
}

int main()
{
    string first_name = "Jacob";
    string last_name = "Landry";


    std::cout << hello(first_name, last_name);
} Variables C/C++ is statically typed, so you declare variables by choosing a type, followed by the identifier, then the value. Functions Functions are defined similarly to variables by starting with their return type, followed by the identifier. Variables must have types as well when passed into the function. Data is returned from the function with the return directive. Output Output, this example, is sent to the terminal with . This uses the “standard” library (std) and the “character out” function (cout).  Strings can be concatenated with the “+” symbol, though I did also see some examples where they could be concatenated with “<<“ though I’m sure this does something slightly more complex so I would want a better understanding of it before I use it. std::cout Go package main

import "fmt"

func main() {
    var first_name string = "Jacob"
    var last_name string = "Landry"

    fmt.Println(hello(first_name, last_name))
}

func hello(first_name string, last_name string) string {
    return "Hello " + first_name + " " + last_name + ", how are you doing today?"
} Variables Variables are declared with the “var” directive followed by the identifier and value. Optionally, you can provide a type as well, after the identifier, but it is not required. It seemed that this could help create default values for variables so they were not null before being initialized: would default to 0, for example. I’m not sure if this is really the use-case for this feature or not, though. Either way, assigning types when possible is a good idea IMO. var number int Functions Functions are declared with the directive, followed by the identifier. While variable types are optional, they seem to be required inside the function and appear after the variable name. You must also provide the function’s return type after the variables if you are returning data. Data is returned with the “return” directive. func Output In this situation we output to the screen with the “Format” (fmt) package. This outputs data to the terminal as in all the other examples. Strings can be concatenated with the “+” symbol. Scala object Hello {

    def main(args: Array[String]) = {
        var first_name : String = "Jacob"
        var last_name : String = "Landry"

        println(hello(first_name, last_name))
    }

    def hello(first_name : String, last_name : String) : String = {
        return "Hello " + first_name + " " + last_name + ", how are you doing today?"
    }

} Variables Variables are defined by the var directive, followed by the identifier and the value. Optionally you can provide a type here with the format after the identifier but before the value. : Type Functions Functions are defined by the “def” directive followed by an identifier. Variables being accepted into the function require types, unlike variable declarations which are optional. It is also required to provide the return type for the function after the list of variable inputs. Data is returned with the “return” directive. Output Output to the terminal is created with , which is clearly very common. Strings can be concatenated with “+” (again, common apparently). println Rust fn main() {
    let first_name = "Jacob".to_string();
    let last_name = "Landry".to_string();

    println!("{}", hello(first_name, last_name));
}

fn hello(first_name: String, last_name: String) -> String {
    return format!("Hello {} {}, how are you doing today?", first_name, last_name);
} This was, by far, the most confusing for me so I’m looking forward to digging into the language more. Variables Variables are defined by the “let” directive, followed by the identifier and a value. It seems that, by default, all variables are immutable, so if you’ll need to modify this variable later you have to add the “mut” directive after “let”. Types are not defined, and as far as I could see in my first searches, there isn’t a way to do so. Functions Functions are defined by the “fn” directive, followed by the identifier, list of params (with types) and a return type signified by “→ Type”.  Data is returned with the (you guessed it) “return” directive. Output Output to the terminal in this case was generated with a println() call. This call takes a format string where variables replace “{}”.  You’ll see I had to use a similar formatter in the return of the function as well. This was how I concatenated the strings. I could not find another way similar to the other examples where you “add” strings together, instead I found that I needed to pass them into this formatter to build the string I wanted. It was confusing at first but I actually like it better. I’m sure there are similar constructs in the other languages that I could go back and employ. There you go! You now have working “hello world” examples in each language, along with examples on variable and function declarations and some basic string concatenation. This basic crash course is a great way to hit the ground running in any language, I’ve found because it lays the building blocks for everything you’ll do in the language in the future. Just to note, something I did not touch on here is how to install and execute these files in each language. Some languages require you to build the code first then execute a binary. Others you can simply execute the code as it’s written. This varies from language to language. I think, in the future, I might write another short series on how to set up/debug each language (as I learn more about that myself) but for now, if you want to run any of these examples yourself either find me on twitter and ask some questions or find the (usually amazing) documentation for the language(s) you want to try. They should have everything you need there to get the language installed on your development machine and start hacking away. Be sure to keep an eye on my profile for the next post. I’ll dive a little further into all of these languages with a look at common data types and how they’re used or implemented in each language. I had planned to provide an article a week but this took far longer to write and edit than expected, but I’ll try to produce them as regularly as possible. I’m aiming for every-other-week, so stay tuned and stay safe!

The All-Purpose Programmer: Episode 1 - "Hello World"

Learning about cryptocurrency and blockchain can be a little overwhelming when you're new to the space. That's why podcasts are such a great medium for breeching the topic. They are usually bitesize, tailored to a niche audience, and give that perfect cosy and intimate feel to the process. Now, I know it's not 2022 yet, but there are a shed load of articles out there already using the 2021 title so I'm getting ahead of the game. The list below is purely my personal opinion, but whether you're a crypto novice or a seasoned shitcoin OG, I hope there's something for everyone! 5. What Bitcoin Did Peter McCormick shot to crypto-stardom when notorious Fake Satoshi, Craig Wright, tried to sue him for calling him out on his bullshit (Peter won the battle, of course). Nowadays, his shows are a little less controversial, but they are solid, factual, and 'The King of Bedford' pulls no punches in his opinions of anything which isn't Bitcoin. 4. UpOnly with Cobie and Ledger The Twitch stream which doubles as a podcast nowadays, I was surprised to see Cobie doing sponsored content, but it's a pretty anarchic affair overall. Famously, they once donated over in one of the most heart warming moments in crypto ever. $800 thousand dollars worth of Ethereum and other cryptocurrency to a child with leukemia 3. Tech Stories with Hackernoon They won't let me publish this article without mentioning their show... I joke. The Tech Stories Podcast with Hackernoon is awesome. Not just crypto, but everything new and techy, with my good friend Utsav Jaiswal representing us blockchain geeks, why haven't you checked it out yet? 2. The Joe Rogan Experience Outside the box, but it is the world's largest podcast. And with guests like Elon Musk talking Dogecoin, and many other actual notable crypto experts appearing to give insights and teach Joe the ways of the chain, there is a whole lot of crypto content scattered throughout. Plus, it's entertaining as hell. 1. The Bad Crypto Podcast Joel and Travis will always hold a special place in my heart. Not just because they're hilarious, but because they are humble. When I first started getting into Bitcoin and crypto, it was the summer of 2017 (if only we could go back and buy more, right!?). The Bad Crypto Podcast was brand new, and as the hosts would constantly remind, they were just two dudes talking crypto. They knew as little as I did, and weren't afraid to ask the questions others might feel silly asking! And with early days interviews with John MacAfee, Jordan Belfort, and many other weird and wonderful guests, it was like they were making the show just for little old me! So, Joel and Travis, if you're reading this (and I hope they will because I'm going to spam them with it!), cheers for the learning and laughs dudamigos! Looking for Something New? I'm not a narcissist or anything, but I did mention I have a crypto podcast of my own (well, it's not just mine but let's not be pedantic). It's not quite in the big leagues with the others above yet, but you never know, one day, right? If you like crypto, bitcoin and blockchain, and also comedy panel quiz shows, it might be right up your street! So check it out and let me know what you think! And if you'd like to be a guest on a future episode, reach out, we don't bite! https://nevermindtheshitcoins.buzzsprout.com/

Top 5 Crypto + Bitcoin Podcasts: 2021/2022 

- When nothing seems to help, I go and look at a stonecutter hammering away at his rock, perhaps a hundred times without as much as a crack showing in it. Yet at the hundred and first blow, it will split in two, and I know it was not that last blow that did it—but all that had gone before. Jacob A. Riis How are post-war industries rebuilt? Can worldwide poverty be reduced? Can couples' divorce rates be predicted? The answer to such dramatic questions is usually innovation. Innovation is a revolutionary process that requires an entirely different mindset to find novel solutions. And yet, innovation is not the only strategy to create dramatic change. If innovation happens quickly, requiring shocking transition and radical adaption, kaizen is an approach to commit to small and comfortable steps towards better circumstances. This (literal translation is “change for better”) is American in origin. It was brought initially to Japan by American statisticians and quality experts such as W. Edwards Deming, who helped Japan rebuild its industry after World War II. Toyota made kaizen famous by adopting the lean manufacturing process and the iterative improvement mindset. So, in the 1980s, kaizen crossed the world back to the United States, mainly in highly technical business settings. Japanese concept Nevertheless, kaizen is also a good strategy for personal self-improvement. Too many times, we tend to overlook the power of small steps. Still, over extended periods, tiny steps will compound into significant results. Instead of focusing on daunting questions and high expectations, we can break these targets into absurdly little milestones that we will try to hit consistently. The kaizen framework The kaizen process looks like this: First, we choose a novel habit that we want to incorporate into our daily routines or an action that makes us feel uncomfortable. Then, we repeatedly ask small questions on how to break this action into small steps. Following insightful answers, we break that activity into tiny little steps. We allocate low time unit commitments (varying from seconds to minutes or tens of minutes) to fulfill them every day easily. Accountability is critical as it helps with setting clear intentions and keeping a straight course. Examples: keeping a journal, reviewing the day in a stoic manner, or having a partner, like a friend, online buddy, psychologist, etc. The goal is to maintain these new activities effortlessly. There are subtle psychological nuances about making the first step. After experiencing the first step, we can decide if it is time to make another. We will know when we are ready, as this process will become automatic. But, if at any level, we find ourselves dreading the activity or making excuses to get rid of it, psychologist Robert Mauer suggests in his book , acknowledging that it is time to reduce the size of the step. After all, life is not a game of perpetual ladders but more a game of snakes and ladders. One Small Step Can Change Your Life: The Kaizen Way Why is repetition necessary? Because repetition does not make it perfect but permanent, whenever we repeat a new behavior, even for just a few seconds, eventually, the brain will stamp those actions as meaningful. As a result, it will create new neural pathways to reinforce those new behaviors. Incremental and bite-size adjustments have a water-like quality: give them enough time, intention, and focus, and changes will start to permeate our lives like water absorbed by dry soil. Why is it essential to ask small questions? Sometimes we are so concerned about failure that we deliberately create vague goals, and thus, nobody can criticize us when we crash. Perhaps it helps to break the impossible questions into more manageable chunks. Asking small questions might also lead to more truthful answers. Focusing on asking questions allows the brain to concentrate on problem-solving. By repeating this behavior long enough, the brain will eventually lead us to valuable and novel insights or exciting approaches to our questions. Why is it important to take small actions? The brain tends to resist change and favors the sweet equilibrium of . But small actions do not look perilous, so we rewire our nervous system by employing tiny steps. Thus, it is possible to hit a new homeostatic state where we fully incorporated and maintain the new habits. homeostasis Examples of kaizen questions What is one small step I could take toward reaching my goal? What is one small step I could take to improve my health (or relationships, career, or any other area)? If you tend to berate yourself with negative questions (Why am I so fat?), try asking: What is one thing I like about myself today? If health were my first priority, what would I be doing differently today? What is one way I can remind myself to drink more water? How could I incorporate a few more minutes of exercise into my daily routine? If you are unhappy but aren’t sure why, try asking yourself this: If I were guaranteed not to fail, what would I be doing differently? This question is for anyone who has a festering conflict with another person, whether a boss, employee, in-law, or neighbor, and is trying to get past this problem. Every day, ask yourself: What’s one good thing about this person? - Robert Maurer, One Small Step Can Change Your Life: The Kaizen Way Examples of kaizen actions – Remove one object from the shopping cart before heading to the cash register. Stop overspending – Stand—yes, just stand!—on the treadmill for a few minutes every morning. Begin an exercise program – Once a day, note where your body is holding tension (your neck? lower back? shoulders?). Then take one deep breath. Manage stress – Pick an area of the house, set a timer for five minutes, and tidy up. Stop when the timer goes off. Keep the house clean - Robert Maurer, One Small Step Can Change Your Life: The Kaizen Way Simply standing on the treadmill will not produce much health. But such tiny changes can bring winds of attitude change as initial resistance starts melting. Mauer writes of his patient, Julie, that she became more animated after standing on the treadmill for a minute daily and wanted to know what else can do on a few minutes per day. Within a few months, Julie found herself exercising even when she didn’t have to. The exponential growth of kaizen habits Usually, to explain exponential growth, teachers use the water lily problem: consider a lake with just one lily pad on a summer day of June 1. Now consider that every day lily pads will double in numbers. By June 30, lily pads will cover the whole lake. Q: When was 50% of the lake covered? A: June 29. As lily pads double in size, the lily pads will go from 50% on June 29 to 100% on June 30. Q: If lily pads cover the lake 100% on June 30 and 50% on June 29, when did the lily pads cover 1% of the lake? A: June 24th. (June 30 – 100%, June 29 – 50%, June 28 – 25%, June 27 – 12%, June 26 – 6%, June 25 – 3%, June 24 – 1%) It took more than three weeks for lily pads to cover just a tiny 1% of the lake and then less than a week to cover the whole lake. Similarly, there might be years behind an “overnight” success as it can take a long time to build a strong foundation and a much shorter time to get from 50% to 100% with kaizen. The negative side of kaizen As this shows, we might be chasing obsessive optimizations. Let’s ask ourselves: are all the improvements we want to apply worth our time, energy, and focus? timeless xkcd cartoon Then, as the saying goes, it can’t be a superpower if it can’t be used for evil. The beginning of addiction usually follows kaizen. Just a drink, just a cigarette, just a pill, something so tiny, so small we would think it would not matter at all. Except that it does, and so, proceed with caution. The marginal gains of kaizen and relieving global poverty In 2019, Esther Duflo, her husband Abhijit Banerjee, and Michael Kramer shared the Nobel Memorial Prize in Economic Sciences “for their experimental approach to “. Their approach to fighting global poverty is based on scientific evidence. They ran hundreds of (trials where participants are randomly allocated to various treatments) to find which approach yields better results. alleviating global poverty randomized controlled trials Scientifically approaching kaizen does not mean making small changes and hope for the best. Instead, it is about breaking a complex problem into small parts. Then, by using empirical evidence from controlled and randomized experimentation, effects are isolated. Thus, it can be established which steps work and which don’t. For example, they discovered that: The study of the long-term effect of deworming children in Kenya, mentioned above, concluded that being dewormed for two years instead of one (and hence being better nourished for two years instead of one) would lead to a lifetime income gain of $3,269 USD PPP. Small differences in investments in childhood nutrition (in Kenya, deworming costs $1.36 USD PPP per year; in India, a packet of iodized salt sells for $0.62 USD PPP; in Indonesia, fortified fish sauce costs $7 USD PPP per year) make a huge difference later on. As Duflo declared to author Matthew Syed: It is very easy to sit back and come up with grand theories about how to change the world. But often, our intuitions are wrong. The world is too complex to figure everything out from your armchair. The only way to be sure is to go out and test your ideas and programs and to realize that you will often be wrong. But that is not a bad thing. It leads to progress. It is possible to make significant progress against the biggest problem in the world through the accumulation of a set of small steps, each well thought out, carefully tested, and judiciously implemented. -Matthew Syed, Black Box Thinking: Why Most People Never Learn from Their Mistakes–But Some Do Kaizen and predicting divorce rates John Gottman is an American psychological researcher who did over 40 years of extensive research on divorce prediction. Analyzing thousands of marriages at their couples laboratory , Gottman has been able to predict with high accuracy ( ) if couples would be happily married or divorced within four years. Love Lab based on experiments, varying from 87% to 94% accuracy One of the groundbreaking findings was that positive remarks outweigh negative remarks with a daily factor of five to one in successful relationships. Long-lasting marriages weren’t about over-the-top grand gestures such as expensive holidays or memorable birthday parties, but they were more about the quiet, almost invisible gestures. Partners were asking each other about their days. They used a pleased tone of voice when the partner called instead of an exasperated or rushed voice. Small gestures of affection are more predictive of loving and trusting relationships, presumably because repeated gentle moments provide consistent nourishing. And as for the name of ? Gottman’s podcast Small things often. This reminds me of what I read in : Maybe You Should Talk to Someone One woman said to her husband, “You know what three words are even more romantic to me than ”I love you”?” “You look beautiful?” he tried. “No,” his wife said. “I understand you.” Kaizen is deceptively simple and utterly brilliant. It would take a lot of focus, failures, regrouping, and trust in the system, not results. To continue working and tinkering on imperceptible changes day after day, year after year, with no 100% guarantee the road we take will lead anywhere. It doesn’t matter. Samuel Beckett Try again. Fail again. Fail better. - . Previously published at https://www.roxanamurariu.com/the-exponential-art-of-kaizen/

Change for Better: The Exponential Art of Kaizen

Lessons learned There’s a lot of buzz around Google Anthos! I’ve lost count of the number of times I’ve been asked “What IS Anthos?!”. Well today I’m NOT going to answer that question! However, what I will do is (hopefully!) help some of you to spin up your very own Anthos bite-size cluster on some relatively inexpensive hardware thanks to the newest “flavor” of Anthos: Anthos on Bare Metal. From there you’ll be able to explore the features of Anthos for yourself, and embark on your own Anthos journey of discovery! Anthos on Bare Metal allows you to spin up a cluster from as few as 2 Intel NUCs. It’s a relatively straightforward process, but the process can be a little daunting if you haven’t been through it before. I encountered a few issues along the way, so what I’m walking you through today is my “path of least resistance”! So let’s start with the hardware. I got sent 3 Intel NUCs. Each of them has a Core I7 Gen 10 processor, 32GB of RAM, and 256 GB SSD. To be more specific, here's everything that I needed to get up and running: 3x Core : 6 cores / 32GB DDR4 Mem / 256GB SSD NUC10i7FNH i7-10710U 8-port GbE switch (optional if you have enough ports on your router / switch) 4xRJ45 CAT5E 16GB USB Stick to install Ubuntu You can install Anthos on Bare Metal on Ubuntu, CoreOS, or RHEL servers. (See more details ). here Out of the box the NUCs come with Ubuntu installed. However, the Kernel version led to issues with Ansible for me, so I started from scratch and created a bootable USB stick that would install “virgin” Ubuntu 20.04 onto each NUC. I will create my cluster out of just 2 of the NUCs (which is really the minimum we’d recommend). The third I will use as my “workstation” in order to bootstrap the cluster using the command-line tool. All is not lost, however, we can add that third NUC to the cluster later, once it’s up and running! bmctl Happy-path install process Connect one of the NUCs to a monitor and keyboard. Boot it up. In the case of the NUCs I received, Ubuntu was already installed. We’ll use this machine as a workstation for now. Open a terminal & (you'll need the password for the default user) sudo su mkdir usb-boot && cd usb-boot Copy over shell script from (Thanks to my colleague Cody Hill for this!) https://github.com/c0dyhi11/ubuntu-20-04-autoinstall/blob/main/build_iso.sh chmod +x build_iso.sh Insert a USB stick into the NUC and note the device that represents the USB stick (i.e. ) fdisk -l /dev/sda (whatever is returned from above) ./build_iso.sh -F /dev/sda Ubuntu ISO will be downloaded and written to the USB stick. N.B. command will finish but writing may continue. Wait 5 mins. Powerdown your NUC. Connect your second NUC to monitor and keyboard. Insert the USB stick created in step 8. Power on. Press until the boot menu shows. Select the USB stick to boot from. If there are multiple partitions, select the first. F11 Ubuntu install will proceed. Select language, keyboard layout, network configuration (strongly recommended to switch to “manual” network config and assign a static IP). I selected not to download updates. For storage config you can just accept defaults. Do not install any other options / software / tools when prompted. You will be prompted to create a username and password. Use whatever you would like, just make a note of it. My suggestion would be to name the machine nuc-1 (and then nuc-2, and so on). At this stage you can elect to copy over an SSH key from Git if you’d like, or you can perform this step later (described in ). step 19 Wait for install / updates to fully complete (“Reboot” will show as option at bottom when it’s done). Reboot. Remove USB stick when prompted. NUC will reboot into Ubuntu. Disconnect monitor and keyboard from NUC. Repeat steps 11-14 for remaining NUC(s). Reconnect workstation NUC and boot up. Open a terminal & generate an SSH key: ssh-keygen -t rsa and copy to clipboard cat $home/.ssh/id_rsa.pub SSH into each NUC in turn (using username and password set in ) Paste in public SSH key and save Perform further steps listed here: step 12 ssh snuc@192.168.1.201 sudo su vi /root/.ssh/authorized_key Configuring Ubuntu | Anthos on bare metal From the workstation NUC validate you can SSH to each NUCvia (N.B use the path to the private SSH key you previously generated, and the IP address of each of your node NUCs). You should be able to connect to the NUC as root without being prompted for a password. ssh -o IdentitiesOnly=yes -i /home/ubuntu/.ssh/id_rsa root@ 192.168.1.231 From the workstation NUC proceed to follow instructions here: . You’ll install and init gcloud and kubectl. Next you’ll install the bmctl CLI tool. https://cloud.google.com/anthos/gke/docs/bare-metal/1.6/quickstart#install-gcloud Proceed to follow instructions here: . We’ll create a hybrid cluster - a single cluster for both admin and workloads, that can also manage user clusters.(other options are standalone, admin, & user clusters). In the hybrid cluster deployment option, one of your NUCs will be the control plane node, the other will be the worker node. You can read more about the other deployment options on . https://cloud.google.com/anthos/gke/docs/bare-metal/1.6/quickstart#create-cluster-nodes Creating clusters: overview Find a “block” of # of NUC contiguous IP addresses on your network. 3 or 4 IP addresses will be enough for the purposes of a POC. Whenever you create a K8s service of type “LoadBalancer”, one of these IP addresses will be consumed. I used 192.168.1.236-192.168.1.240. The “start” IP address (i.e. 192.168.1.236) you will specify as your ingressVIP in the YAML file. You should also find another free IP address for your controlPlaneVIP. It should NOT be in the start-end range discussed above. I used 192.168.1.196. Specify one of your NUC IP addresses as the IP address for the control plane node pool, and the other(s) right at the bottom of the YAML as the worker node pool. Run to perform preflight checks, and then to install Anthos. If you followed the instructions in step 22 then all APIs and service account keys would have been generated for you. ./bmctl create cluster -c [name of your cluster] Once it completes successfully you’ll be given the path to the kubeconfig to connect to your new cluster. [Optional] If you wish you can re-image your “workstation NUC” using the USB key, and add it as a worker node to your cluster kubectl -n my-cluster edit nodepool np1. Remember to copy your “baremetal” folder (and subfolders) off to somewhere else first (because you’ll need the kubeconfig file!). Running Your First Workload So now let’s deploy a simple “Hello world!” Kubernetes deployment and service. Let’s begin by setting our environment variable. puts the under . So, for instance, I called my cluster bm-cluster: KUBECONFIG bmctl KUBECONFIG bmctl-workspace/[your-cluster-name]/ export KUBECONFIG=$HOME/baremetal/bmctl-workspace/bm-cluster/bm-cluster-kubeconfig First let’s create a deployment YAML: apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-deployment
spec:
  selector:
    matchLabels:
      app: metrics
      department: sales
  replicas: template:
    metadata:
      labels:
        app: metrics
        department: sales
    spec:
      containers:
      - name: hello
        image: 3 "gcr.io/google-samples/hello-app:2.0" Save the file (as my-deployment.yaml), and now apply it to our Anthos BM cluster: kubectl apply -f my-deployment.yaml Let’s check the deployment: kubectl get deploy You should see something like this: NAME            READY   UP-TO-DATE   AVAILABLE   AGE my-deployment   3/3     3            3           30s Now let’s create a service of type LoadBalancer. Save the following YAML as my-service.yaml: apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: metrics
    department: sales
  type: LoadBalancer
  ports:
  - port: targetPort: 80 8080 Now let’s apply it: kubectl apply -f my-service.yaml Now let’s validate it got created: kubectl get svc my-service You should see something like this: Notice how my-service's IP address (external-IP) got automatically drawn from the “range” we specified earlier. Now we can curl our service, and check that everything is working as it should: For me, I , and I see: curl 192.168.1.237 Hello, world! Version: 2.0.0 Hostname: my-deployment-68bdbbb5cc-mpftc Your Anthos on BM cluster is fully operational. It will also show up in the Google Cloud console. Congratulations! If you want to log in to your cluster (to view node and workload information from the console) you can now follow the instructions on . Logging in to a cluster from Cloud Console

How to Install Anthos Bare Metal on Intel NUCs: A Beginner's Guide

Introduction Virtual brainstorming is a really important skill to master for distributed teams that potentially can have a huge collaborative payback. Participants come away feeling and the organisation benefits from the uncovered. Virtual does come with a that don't exist in the off-line world. heard, valued, aligned actionable items unique set of problems Setup It is necessary to have , for communication, white-boarding and polling. I used zoom and jamboard, but any equivalents will do. group collaboration tools and break it down into logical bitesize chunks in advance e.g. (DevOps - technology, process, culture and people or simply performance for more focus). to invite (be diverse, consider bringing in x-org experts). Consider the total number of people, if you have more than 8-10 invitees then you need to lined up to help you run the sessions. Identify your focus area Identify your audience get assistants Identify you require, It depends on how focused the topic is. The session I ran was broad, took two hours and it was time squeezed with sixteen participants. how long , so you can control breakout rooms and polling options. Be the owner of the zoom invite Send out an invite in advance with an intro, pre-reading materials that sets the correct mindset for participants. Create a group chat channel (e.g. slack). Create a virtual post-it board (e.g. Jamboard/Miro). Set up a feedback form for circulating at the end (e.g. pollev, google forms, survey monkey).  Use an open feedback question in your post survey to avoid leading the pollsters. I used "Share your feedback on todays brainstorming session and upvote entries that you think are most important" Have a pre defined agenda to drive the session and put time duration against the agenda items Agenda - Intro (Who we are, Why are we doing this, Whats the outcome we are going to achieve) Two mins for individual brainstorming Eight minutes for group brainstorming using Jamboard/Miro Five minutes to organise into themes Two minutes to vote on the order of themes to discuss (aim for 3) Five to ten minutes to discuss each theme and identify metrics Five minutes to fill out session feedback (pollev/zoom polls) Controlling the Meeting At the , give everyone time to about different issues, then have participants add their post-its and "initial" them (so you can refer back to original poster for clarifications). This is a for the most part. beginning think separately silent activity set the expectation of the amount of time to be spent on different parts of the process. Utilise a count-down timer with an audible alarm for the first three stages, but a silent countdown later thats visible and shared screen to allow participants to self regulate.  Demonstrate flexibility on the times if things are flowing well, there is a balance to be achieved between interrupting to move things along, and allowing things to bubble up. During the meeting If the participants are going too deep (solutioning) or off-topic, then allow the conversation to develop to the point you can . Write down the info on a different coloured post-it on the Jamboard for all to see and move to next item. How to decide on when to interrupt? note down the bones of the discussion and interrupt Request the with the grouping of the post-its into common themes and it is easier to process if you add post-its in different colours to capture the themes. Use zoom's poll function to prioritise the selection order of the themes. Go through the post-its in the chosen topic (under the selected theme) one by one and allow discussion to occur. Capture points made and actions using separate notes (new colour on the Jamboard). volunteer assistants to help The of the meeting begins when there is approx. ten minutes left. end phase Mark the time out loud to inform the participants Bring the current discussion to a close Ask if any participant has something they really want discussed and allow them that time. End the meeting by thanking the participants and pointing them to the survey for feedback. Post Meeting Go through the jamboard notes and write up a report and actions. Arrange for a small group to review the report prior to circulating it to the correct audience and delegate the actions. There may be follow-ups required with certain people (initials come in handy now) for more information on interrupted topics. Brainstorms takes preparation and practice to maintain momentum and focus, however if you follow and adopt some or all of my suggestions it can be very rewarding in terms of insights and knowledge sharing from the participants.  Whatever the actionable outcomes of the brainstorming session it will have achieved better collaboration, awareness and engagement of the participants. Conclusion:

Virtual Brainstorming Success Guide

On Friday, I attended this webinar by Atlassian on Enterprise Agility and Innovation at Scale. The event highlighted how a bank of 160 years with processes embedded deeply into their core business is transforming itself. Through changing its leadership mindsets, teams formation, focus on customers, process improvements and usage of Agile tools, etc. It showcases how they embrace the concept of AGILE effectively and partially highlight their digital transformation journey.‍ Link To The Event Recording The AGILE concept is not new in Technology. For many traditional enterprises and small-medium businesses (SMBs), their business has been product-driven, industry-focused, service-oriented, margin-driven, etc. As the market evolves and changes digitally (accelerated by the pandemic), these traditional businesses are being disrupted by new entrants and more innovative business models. It inspired me to represent my thoughts pictorially and create the concept of AGILE DNA. This simplified DNA Sequence shows the inter-connected sequences between Teams, Customers, Products and Culture, which form the basis in adoption of AGILE in your business. NOTE: It is arguably NOT a complete representation but an abstract form of the AGILE Concept. ‍ First, Our Teams The diagram reads from left to right, starting with Teams (leaders, co-workers and partners). The fabric of a company is its people, how the 'mission' of the company should focus on their teams. How leadership embraces AGILE is an essential piece of the puzzle. Their ability to rally the teams, break norms and lead the change helps overcome the stigma of change. Change is always difficult, even AGILE thinking, changing mindsets across teams is not a trivial task. These points were validated during the event by the panellists. They shared the importance of team's being open-minded, self-directed, having purpose and having management commitment. Next, the AGILE DNA connects Teams to Customers Why do we put customers first and not the products? We emphasize customers before products, as our focus is to 'build with' customers. Focusing on customer's needs (i.e. being customer-obsessed), empathizing with their business objectives and requirements, thereby putting theirs in alignment with ours. Where the business may serve a variety of personas, demands and requirements, having a 'build with' approach towards these personas (collectively where possible), sets up processes to capture these demands/requirements early, and improve customer satisfaction. This customer-centric approach helps to objectively align the Teams focus when they build their product. Referencing  #1 of 12 . The focus of satisfying the persona your product is built for, is essentially being AGILE. Principles of AGILE One of the panellist's from the event also shared how objectives key results (OKR) of their product is aligned with their customer's metrics (KR) and other aspects of customer satisfaction i.e uptime/etc. It is also highlighted on the Bank's website under Innovation Culture. from Standard Chartered website (update 29 Aug 2020) Link Thirdly, We Move Our Focus to Products What is the differentiation of a great product? Obviously, there is a lot to cover about this topic and we believe there are experts (within/outside) who are more qualified to address your product development. However, we want to highlight certain essential qualities that product development through AGILE's 12 Principles should try to achieve. Principle #2 - Changing Requirements. Product evolves around customer requirements more so to enhance the competitive advantage. Principles #4, #5, #11 - Diverse, self-organizing Teams working on the project. Motivated to look at the problem (1/2 full, instead of 1/2 empty). Having a trusting and supportive leadership. This again was reiterated by the presenter during the event. He shared how Atlassian allows employees to cross-pollinate creatively without restriction during a special day of the year, trusting them which leads to improvements in product quality and new innovative ideas across different business units. Principles #3, #7, #9 - Delivering a working product frequently with good design principles will help the product evolve to greatness. = The Sum of All Culture Company Culture via Wikipedia was first conceptualized in 1951 by Dr. Elliot Jaques. It defines organizational culture, encompasses values and behaviors that contribute to the unique social and psychological environment of a business. For AGILE DNA the culture that the company needs is the sums of the parts; Collaborative Teams, Customer Focus, Product Agility and most importantly Company leadership, an open and "walk the talk" kind of leaders. ‍The ↗️ represents the interplay of directions in the diagram. It also signifies the iterative loop or feedback nature of the AGILE concept. How Should a Company Get Started? There are so many ways to learn on this journey. One I think quite useful and easy to start is Atlassian's Team Playbook. The playbook gives short bite-size tips on play to strength AGILE knowledge and team collaboration. To Access Team Playbook Click In summary, the concept of transformation through AGILE DNA is guidance to companies who are trying to tackle this disruption. By prioritizing their focus to strengthen their teams and leaders within, through open and committed leadership; they put a relentless focus on the customers, building products by design to cater to such focus and feedback. I hope this article provides a spark for any company who aspires to transform in this disruptive situation. ‍ About the author Stephen Chan is the founder of ASIA Technology Ventures. As a creative and entrepreneurial thinker with deep knowledge in both business and technology, he has accomplished more than 2 decades of high-growth businesses, developed revolutionary Go-To-Market strategies and led & built teams across the Asia Pacific. He has a strong understanding of technology like SaaS/Cloud infrastructure, mobile apps, developer tools, blockchain and artificial intelligence. Asia Technology Ventures was founded to help Asia's businesses transform, scale and compete beyond their comfort zone. This is driven by his personal belief that "Success is a journey, not a destination", in-line with his pursuit of helping others as the foundation for true, happy, and meaningful living. Previously published at https://www.asia-techventures.com/post/agile-dna-genetics-digital-transformation

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