Brainify

As a seasoned tech entrepreneur, Ivan Mishanin co-founded and served as the CEO of Bright Box, a globally connected car software vendor, for over 8 years. The company reached a staggering $10M in annual revenue before being acquired by Zurich Insurance Group, which was ranked 79th in the Forbes rating. Global 2000 Currently, Ivan is the COO and co-founder at , a cutting-edge company revolutionizing the drug development process for depression and other psychiatric disorders. Brainify.AI In the interview, we discussed the challenges of being a tech entrepreneur and key strategies of transforming the company’s growth around the globe. Let’s talk about Bright Box. What market did you start with, and when? I established Bright Box in 2012 as an IT company that focuses on solutions and platforms in the connected car market. By the end of 2014, we were in the final stages of negotiations with a few Eastern European auto companies. We also managed to secure $1.5 million in investments from the LETA Capital venture fund. However, at the time, the Eastern European markets experienced a downturn that became a real issue for our business. To bounce back and find clients quickly, we leveraged all available channels, including networking through friends, participating in exhibitions, and attending conferences worldwide. At one such event, the Connected Cars conference in Amsterdam, we met potential partners from the Middle East, and after seven months, we won our first significant contract with Nissan in the region for three years and 45,000 cars. How did you transform the company once it started developing in other markets? As we began expanding into other markets, we had to transform the company in several ways to adapt to the unique challenges and requirements of each region. Here are some of the changes we made: Adjusting our team's schedule. Since the work week in the Middle East is from Sunday to Thursday, we adjusted our team's schedule to provide support on Sundays. This required some effort and incurred minor additional costs, but it was crucial for meeting our clients' expectations in the region. Adapting our product to the market. We began focusing more on achieving a better product-market fit by tailoring our product to the specific needs and demands of the new market. This involved understanding the unique preferences and requirements of local customers and adjusting our offerings accordingly. For instance, we added right-to-left writing and Arabic language support for the Middle East market, and we integrated Baidu Maps and the Chinese version of Microsoft Azure for the Chinese market. Restructuring the product team. As our growth continued, we realized that having a single product team for all regions was not feasible. To address this issue, we split the team into two parts: Core and Regional. The Core team was responsible for implementing technological and infrastructure changes that would simplify market adoption, while the Regional teams were responsible for catering to the specific needs of each country or region. Overcoming language barriers. Not all of our team members, including myself, spoke fluent English at that time, so we had to work on improving our language skills to facilitate effective communication in the international market. To improve my English, I began watching movies exclusively in English with subtitles during my flights, which helped me become familiar with conversational language. Additionally, I changed the language settings in all my operating systems and software to English and used a dictionary on my iPhone to look up unfamiliar words. I also started reading books in English, which helped me expand my vocabulary and grasp sentence structure. Traveling. In 2014-2015, I took around 100-120 flights per year to support our expansion efforts and maintain close communication with our teams across the globe. Learning about local culture and market needs. My team and I began investing more time in understanding the culture and specific needs of the market we were entering. This knowledge allowed us to better connect with our customers, partners, and stakeholders in the region and ultimately helped us succeed in our international expansion efforts. Wow, that’s a lot of traveling! What are a few crucial things you did when expanding Bright Box into new markets? Did you have to move to the country yourself? It's essential to prioritize and make some tough decisions. As a founder, you don't necessarily have to move to the country yourself. However, in my case, I decided that the success of the company was my top priority — even more important than my personal life or family. As a result, I made sacrifices in my personal life to focus on the company's growth. For many years, I didn't have a stable home. I lived out of my suitcase and occasionally spent weekends in an apartment. This level of commitment and dedication was necessary for me to ensure the success of our international expansion. To keep our team motivated and focused on the goals, I did everything I could to convey the importance of our work and why we needed to put in the extra effort. I believe that this motivation and a shared sense of purpose played a crucial role in our eventual success in expanding into new markets. In 2017, Zurich Insurance Group acquired your company, Bright Box, for $75M. Can you share some of your experience of how it feels to grow your company from scratch and be able to sell it to one of the largest companies in the world? As the ex-founder and ex-CEO of Bright Box, I can attest to the fact that building a company from scratch and eventually selling it to a global giant like Zurich Insurance Group for $75 million in 2017 was an emotional rollercoaster filled with unique experiences and valuable lessons. Initially, I was over the moon with excitement, knowing that all the hard work had paid off and our company was being acquired by an industry leader. However, this excitement was soon followed by a profound sense of loss and emptiness. It felt like I had given away a part of myself, something I had nurtured and grown from the ground up. The realization that I was no longer in complete control of my "baby" hit hard and left me feeling adrift for some time. After the acquisition, I found myself in a familiar yet changed role. While I continued as the CEO of the company, my decision-making power was reduced, and the ability to make bold moves to propel the company forward quickly became more limited. Nevertheless, I believe that selling your company can be the right decision and a new chapter in your life. Based on my experience, I would like to offer the following advice to those who aspire to a successful exit: Embrace change: Understand that selling your company is a significant milestone and the start of a new phase in your life. You may venture into new industries or explore new spaces, but remember that your experience as a founder will always stay with you. Be prepared for a shift in dynamics: As a founder, you're used to having control and making critical decisions. However, after an acquisition, you may need to adapt to a new corporate structure where you have less autonomy. Prepare yourself mentally for this transition. Negotiate a reasonable transition period: When selling your company, try to avoid a lengthy transition period. As a founder, your entrepreneurial spirit might not align with the role of an employee, even if it's at the company you founded. Ensure that the terms of the acquisition allow you to move on to new ventures in a timely manner. In conclusion, selling a company can be a bittersweet experience, filled with emotional highs and lows. Embrace the change, learn from the experience, and continue to innovate as you embark on your next entrepreneurial journey. Let’s talk a bit about your current project, a deep tech startup, Brainify.AI? How did the idea come about? In 2020, I experienced depression firsthand and was taken aback by the subjective nature of psychiatry. When you have chest pain, a doctor will scan your heart and conduct blood tests, but no one was able to scan my brain. My co-founder Mariam, a neuroscientist from Harvard, had pitched the idea of precision psychiatry to me earlier, but it didn't resonate with me at the time. However, once I went through depression and started feeling better, I immediately recognized the huge need for a more objective approach to mental health. Brainify.AI is an AI/ML electroencephalography (EEG) biomarker platform for depression treatment response prediction that increases the likelihood of new drug approval by 80% and reduces R&D costs. Our products, PlaceboInsight AI, TherapyInsight AI, and CleanSpectrum AI, are designed to enhance the efficiency of clinical trials and improve patient outcomes. We identify biomarkers that predict treatment response during clinical phase 2, use them to select patients for clinical phase 3 trials, and predict placebo response in phase 2 and phase 3 to increase the likelihood of drug approval. Brainify.AI’s long-term goal is to be the go-to biomarker platform for drug development and in-clinic diagnostics. Our product relies on two decades of scientific research in psychiatry, neuroscience, and psychopharmacology. Like many other scientists, we want to be able to recommend the best treatment approach for different groups of patients. However, most importantly, we want to identify “state-markers,” which can be used as the indicator of the treatment results and patient state. By predicting treatment responses, Brainify.AI can become the go-to biomarker platform for drug development and in-clinic diagnostics, ultimately impacting over one billion people affected by psychiatric disorders worldwide. To achieve this goal, we are collaborating with scientists from institutions like Harvard Medical School, Cornell University, Massachusetts General Hospital, and Northwell Health. What are the next goals for Brainify.AI? As we accumulate more data and refine our algorithms, we can expect a future where mental health treatment is no longer a one-size-fits-all approach, but rather a highly personalized experience designed to maximize recovery and overall well-being. Companion diagnostics have proven to be incredibly effective in oncology, and I expect to see the same situation play out in CNS over the next 10 years. We are now at the forefront of this shift toward personalized medicine in mental health. Our cutting-edge AI and machine learning techniques allow us to develop innovative solutions that can help people lead happier and healthier lives. This revolution in care will not only increase the success rate of depression treatments but also offer hope and a better quality of life to millions of people around the globe. We are excited to be a part of this journey and look forward to the advancements yet to come.

From Connected Cars to Mental Health: Ivan Mishanin's Journey as a Co-Founder and Tech Leader

According to the , an autonomous vehicle refers to one that uses technology to partially or entirely replace the human driver in navigating a vehicle from an origin to a destination while avoiding road hazards and responding to traffic conditions. With human error accounting for , relying on this sophisticated technology to keep us safe has become paramount. University of Michigan Center for Sustainable Systems 94% of road accidents As a result of major key players adopting this idea and how widely accepted it has become, the autonomous car market value is expected to grow from an estimated , growing at a CAGR of 22.75% during the forecast period. In this feature, we’ll be exploring the impact of self-driving cars on daily living and what effects it has on passers-by in our neighborhood. USD 41.10 billion in 2024 to reach USD 114.54 billion by 2029 How Self-Driving Cars Work Self-driving cars are not just vehicles; they're tech marvels on wheels. At the heart of their wizardry are sensor systems with superhero-like perception capabilities. Imagine a car that "sees" its surroundings better than you do. Cameras, lidar, radar - these are its vigilant eyes. They work together, creating a dynamic 360° vision that spots pedestrians, obstacles, and even that rogue squirrel about to dart across the road. Now that the car can see, what does it do with all that information? The brilliance of comes into play. These algorithms process the influx of data in real time, making split-second decisions that can outshine the most seasoned human drivers. It's not just about avoiding collisions; it's about choosing the smoothest route, considering traffic patterns, and predicting the unexpected. In essence, the car becomes a brainiac on wheels, making decisions that are both efficient and, more importantly, safe. Artificial Intelligence (AI) algorithms But wait, there's more. Self-driving cars aren't loners; they're socialites. They talk to each other and to the world around them through advanced connectivity and communication infrastructure. Your car being intelligent, is aware of its surroundings, communicates with other cars, traffic lights, and even the city infrastructure. It's a symphony of data sharing, ensuring that every vehicle is on the same page. This optimizes traffic flow and adds an extra layer of safety. Your car knows what my car is doing, and together, they move through the streets harmoniously. Pedestrian Safety Concerns In bustling city life, the increasing number of pedestrian accidents is a worry. Data reveals a consistent rise in incidents where pedestrians grapple with the complexities of traffic. These accidents happen everywhere, illustrating the challenges in . keeping pedestrians safe From fractures to internal bleeding to concussion or head injuries after a car accident, the list of events that could play out after an accident remains endless, leading to an increasing number of fatalities among pedestrians, car owners, and even passengers. , 1.19 million people lose their lives globally as a result of road traffic crashes and around 20 to 50 million people suffer non-fatal injuries, with many incurring a disability. In 2019, the United States alone recorded around vehicles involved in crashes, with over half that volume being passenger cars. As the figures continue to increase, it has marked a consecutive increase in deaths involving motor vehicles. Deaths increased to following an 8.3% increase in 2020.  In 2021, 46,980 people died in motor-vehicle crashes compared to 42,338 in 2020 and 39,107 in 2019. According to W.H.O reports 12.15 million 11% in 2021 Human Factors in Pedestrian Safety Beyond the numbers lie the intricate dance of human behavior on the streets. Understanding how pedestrians interact with traffic is crucial. Human factors, including distractions and unpredictable actions, contribute significantly to the risks pedestrians face. Navigating this human element requires a delicate balance between technological innovation and an understanding of the nuance of our behavior on the streets. Educational and Awareness Initiatives Education becomes the beacon guiding both pedestrians and technology. Initiatives aimed at raising awareness about safe practices for both pedestrians and drivers are integral. Simple measures, such as emphasizing the importance of designated crossing zones and the dangers of distractions, can significantly contribute to a safer environment. It's not just about teaching pedestrians to navigate the streets safely; it's about fostering a mutual understanding between humans and the technology they share the road with. Is The Effort Worth It? Automated driving is becoming widely accepted and this has led to improvements in the technologies involved, ensuring greater efficiency. Some of the exploits that have been seen with its development include: Improved Reaction Times Picture this: a pedestrian about to step onto a zebra crossing, a detects the intention instantly. No distracted glances at smartphones, no momentary lapses in attention - just an instantaneous reaction. Self-driving cars, powered by lightning-fast algorithms, promise to shrink reaction times to mere milliseconds, potentially averting collisions and ensuring a swift response to the unpredictable dance of urban foot traffic. self-driving car equipped with sensors Enhanced Communication with Pedestrians Communication is the lifeblood of safe urban mobility. Self-driving cars are not just silent operators on the asphalt; they are communicative allies. Equipped with advanced signaling systems, these vehicles can convey their intentions clearly to pedestrians. A subtle glow, a gentle hum, or a nuanced display could herald the car's next move, fostering a harmonious play between man and machine at the crossroads of city life. Reduction in Pedestrian Accidents At the heart of it all, the ultimate goal is a drastic reduction in pedestrian accidents. With the precision of technology, the watchful eyes of sensors, and the nimbleness of predictive algorithms, self-driving cars aim to be guardians of the crosswalk. As technology evolves, the vision is a future where the statistics of pedestrian accidents dwindle, creating streets that are not just pathways but sanctuaries for those on foot. Predictive Analytics for Safer Interactions Peering into the future is not just a human trait anymore. Self-driving cars, armed with , assess the environment with a discerning eye. They can anticipate pedestrian movements based on historical data, sensor inputs, and real-time observations. This foresight allows the vehicle to adapt and navigate the streets with a level of anticipation that traditional vehicles might lack, creating a safer environment for those traversing on foot predictive analytics Legal and Ethical Considerations Regulatory frameworks play a pivotal role. Governments worldwide are grappling with the need to update traffic laws designed for human drivers to accommodate self-driving cars. Striking the right balance between encouraging innovation and ensuring public safety is delicate. The challenge is not only to keep pace with technological advancements but also to create adaptive rules that foster growth while prioritizing safety. As we witness a patchwork of regulations emerging globally, the quest for standardized guidelines remains a priority. As machines take the wheel, the question of liability becomes paramount. Traditional fault attribution based on human error is no longer a straightforward matter. Who takes responsibility when algorithms are in control? Liability issues surrounding autonomous vehicles extend beyond manufacturers to include software developers, service providers, and even vehicle owners. Navigating this complex web requires a nuanced understanding of accountability. While it is still very much a debate, it is proposed that should be held liable for all crashes involving their vehicles, “regardless of fault, cause, or comparative negligence. autonomous vehicle makers furthermore, Ethics, a cornerstone of human decision-making, is now encoded into algorithms. The ethical considerations in autonomous vehicles extend beyond the basic rules of the road. When faced with a moral dilemma, such as a choice between protecting the car's occupants or pedestrians, how do we program the vehicle to make the right decision? The challenge is not only to develop algorithms that adhere to ethical principles but also to establish a consensus on what those principles should be. As we entrust machines with decision-making power, the dialogue on ethical programming becomes imperative. Final Thoughts Self-driving cars meeting pedestrian safety is like a big crossroads in how we travel and live. We see that bringing them together is good, and if we use their full potential, it can make a big difference. But, it's like walking a tightrope between making cool tech and keeping everyone safe—drivers, riders, and people walking. So, making sure we keep improving self-driving tech, thinking about ethics, and putting safety first is super important to keep our human lives valued.

Self-Driving Cars and Pedestrian Safety: Crossing the Intersection of Technology and Human Life

In my , we saw about Low-Rank Adaptation or LoRA. LoRA is quite effective for deploying large models and is also fast for inference, thereby solving the inference problem of fine-tuned LLMs. However, when it comes to training, LoRA doesn’t do the trick. For example, to fine-tune a LLAMA 65 billion parameter model, LoRA needs 780 GB of GPU memory. That is about 16 A40 GPUs. The answer to this problem lies with QLoRA, where Q stands for Quantisation. The main motivation for QLoRA is to achieve fine-tuning on a single GPU. previous article QLoRA does this with only three innovations namely: 1. (NF4), a new data type that is information-theoretically optimal for normally distributed weights. 2. to reduce the average memory footprint by quantizing the quantization constants. 3. to manage memory spikes. 4-bit NormalFloat Double Quantization Paged Optimizers In this article, let's look at all three of these novelties and understand QLoRA. Visual Learning If you are a visual learner like me and would like a video version of this article, you may find it on YouTube: https://youtu.be/6l8GZDPbFn8?si=Dx7ubp9bLpb-_lTP&embedable=true Quantization Let's start with Quantisation, which is fundamental to QLoRA. Simply put, quantization works by rounding and truncating to simplify the input values. For the sake of simplicity, consider we are quantizing from Float16 to int4. Int4 has a range of -8 to 7. As we only have 4 bits to work with, we can only have 2 power 4, which is 16 bins to quantize into. So, any input float value needs to be mapped to the centers of one of these 16 bins. Getting into neural networks, the inputs are tensors, which are large matrices. And they are usually normalized between -1 and 1 or 0 and 1. Let's consider the case of a simple tensor with 3 values, say, -0.976, 0.187, and 0.886. We are lucky with this example as the values are distributed equally across the normalized range. When we quantize to int4, each of the 3 numbers takes a unique bin. One problem with quantization is that of outliers, motivating for blockwise quantization Let's take a slightly different example where the input values are no longer equally distributed in the input range. Let two inputs be close together, with one far apart. If we now quantize to int4, the first two numbers fall in the same bin. while the third one is fine. Oh, we don’t want this because, if at all you want to dequantize and convert back to float16, the two numbers no longer convert back to unique values. In other words, we lost valuable information through . quantization error Blockwise Quantization Blockwise quantization, where we divide the input range into blocks. The illustration is with three blocks, each having its quantization constants. One way to overcome this problem could be to divide the input range into separate blocks. In this example, we have three blocks. And we quantize each block separately with each having its own range. So now, the two values that are close together find different bins inside a block. And the third one never had a problem, so it's fine. By dividing into blocks, we independently quantized each block and so each block comes with its quantization parameters, which often is the quantization constant c. In this example, they are c_1, c_2 and c_3. What we just saw is which we illustrated with three blocks. block-wise quantisation, But practically, QLoRA uses a block size of 64 for the weights for high quantization precision. NormalFloat Talking of the weights, one of the interesting properties of pre-trained neural network weights is that they are normally distributed, and centered around zero. Re-weighting of the quantization values with a normal distribution leads to NormalFloat datatype This means that there is a very high probability for values occurring closer to zero rather than around -1 or plus 1. But our standard quantization to int4 is not aware of this fact. And so goes by the assumption that each of the 16 bins has an equal probability of getting the values. To address this problem with standard quantization, we can develop a slightly specialized type of quantization that considers the normal distribution of the neural network weights. This is exactly what QLoRA does, and it names it k-bit NormalFloat. In NormalFloat, the bins are weighted by the normal distribution, and hence, the spacing between two quantization values are far apart near the extremes of -1 or 1 but are closer together as we get closer to 0. To throw some additional light, the green dots show the 4-bit NormalFloat quantization versus the standard 4-bit quantization shown in the blue dots. Let's now move to the next contribution of the paper, which is double quantization. Because the motivation of QLoRA is to train on a single GPU, it is essential to squeeze every bit of memory as possible. If we recall blockwise quantization, we saw that we use 64 blocks to quantize the weights, and each of these blocks has a quantization constant *c*. So, double quantization is the process of for additional memory savings. And through double quantization, we gain half a bit per parameter on average. quantizing the quantization constants The last bit of the puzzle is paged optimizers. Paged Optimizers Paged Optimizers use the CPU memory whenever there is an overflow of memory on the GPU, thereby coping with long sequences. Paged Optimizers prevent memory spikes whenever we abruptly get a long input. Let's say we are working with documents, and suddenly, we have a long document. When we use a single GPU for training, this spike in sequence length generally breaks the training. So to overcome this, the state of the optimizer, say, Adam, is moved from the GPU memory to the CPU till the long sequence is read. Then, when the GPU memory is freed, the optimized state is moved back to the GPU. At a high level thats what happens if we leverage paged optimizers. In terms of the implementation, the paged optimizer is part of the bits and bytes library. And you can enable or disable it during your QLoRA training by simply setting the flag is_paged on or off. Putting together the above-mentioned three components, QLoRA efficiently uses a low-precision storage data type, in our case usually 4-bit, and one computation data type that is usually BFloat16. What does that mean? Going back to LoRA, it means that to optimize for the memory, the weights of the model are stored in NF4. This enables us to load the weights into a single GPU, and the loaded weights are converted into BFloat16 for the computation of gradients during backpropagation. To link it to LoRA, let's look back at this equation from LoRA, where is the input, and is our pre-trained model weights. and are low-rank matrix decompositions. x W_0 A B With QLoRA, our input is . Our weights are stored as (NF4). During the computation of gradients, our weights and quantization constants go through a double dequantization, which is the reverse of double quantization. It happens by first dequantizing the quantization constants and . Then, using the constants, we once again dequantize the weights to BrainFloat16, which is used to compute the gradients. x BrainFloat16 Normal Float4 c_1 c_2 If you are wondering how good is NormalFloat and double quantization, the authors of QLoRA experimented with four datasets and showed that in all four cases, using normal float and double quantization improves the mean zero-shot accuracy of training compared to simply using float. In terms of the GLUE score, QLoRA can replicate the accuracy of 16-bit LoRA and full-finetuning. The authors conclude that 4-bit QLORA with NF4 data type matches 16-bit full finetuning and 16-bit LoRA finetuning performance on academic benchmarks with well-established evaluation setups. So, if you are interested in finetuning on a single GPU and would like the fine-tuned model to match the performance of standard finetuning on multiple GPUs, then QLoRA is the way to go. I hope that was a useful insight into QLoRA. I will see you in my next. Until then, take care. Also published . here

QLoRA: Fine-Tuning Your LLMs With a Single GPU

How to Effectively Evaluate Your RAG + LLM Applications

কীভাবে কার্যকরভাবে আপনার RAG + LLM অ্যাপ্লিকেশনগুলিকে মূল্যায়ন করবেন

So bewerten Sie Ihre RAG- und LLM-Anwendungen effektiv

Cómo evaluar eficazmente sus solicitudes RAG + LLM

Comment évaluer efficacement vos candidatures RAG + LLM

अपने आरएजी + एलएलएम अनुप्रयोगों का प्रभावी ढंग से मूल्यांकन कैसे करें

RAG + LLM アプリケーションを効果的に評価する方法

RAG + LLM 지원서를 효과적으로 평가하는 방법

Como avaliar efetivamente suas inscrições RAG + LLM

Как эффективно оценить ваши заявки на RAG + LLM

RAG + LLM Başvurularınızı Etkin Bir Şekilde Nasıl Değerlendirebilirsiniz?

Cách đánh giá hiệu quả các ứng dụng RAG + LLM của bạn

如何有效评估您的 RAG + LLM 申请

If you're looking for a real challenge, look no further than the world of programming languages. Sure, you could play it safe and stick with the likes of Python or JavaScript, but where's the fun in that? If you're a thrill seeker and up for a challenge, you're in luck because I'm here to give you a rundown of the top most mind-bending languages out there. programming But remember, learning these languages is not for the faint of heart. You need to be ready for some brain exercise! Table of Contents What Are The Hardest Coding Languages? C++ Malbolge Haskell INTERCAL BrainF**k COW Whitespace Want To Learn Some Real Programming? Frequently Asked Questions Is C++ the hardest programming language? Which is harder, C or C++? Which is harder, C++ or Python? Which is the most difficult programming language? Learning the hardest programming languages in the world is similar to art in certain aspects. You begin with a blank canvas and basic raw materials. By combining science, art, and craft, you decide what to do with them. You sketch an overall form, paint the background, and then fill in the details. You continuously take a step back and look at what you've done with a critical eye. But then, you'll toss aside a canvas and start over. In simple words, the hardest programming languages to learn leave your brain in stubborn nodes and intricate code. However, learning any programming language, no matter how basic or complicated, requires time and effort. As a result, it is necessary to undertake significant research and choose a programming language suitable for the desired career route. In addition, a programming language's market value and usefulness have nothing to do with its complexity or convenience. Your final decision should depend on the usability of the language. The ability to make logical decisions using a query language and serial statements is analogous to computer programming languages, which is an advantage. What Are The Hardest Coding Languages? When we talk about c, c++, Java, Python, and other programming languages, there are some that are not only tough but completely unintelligible to most software engineers. They have built a reputation as esoteric programming languages (or esolang). Here is a detailed list of the top hardest programming languages to learn in 2023: C++ Bjarne Stroustrup developed , a general-purpose programming language,  as an extension to the C computing language. Development teams frequently utilize it in in-game and online development, as well as machine learning and data mining applications. C++ However, high-level languages like Python, Java, and JavaScript appear to have surpassed C++ in popularity and usage in recent years. This prompts developers to wonder: Is C++ hard to learn? The quick answer is yes! Because of its multi-paradigm nature and more complex syntax, C++ is difficult to master. While it is well renowned for being particularly tough for novices, it is equally challenging for programmers with no prior expertise with low-level languages. Malbolge Malbolge programming language robbed the mental peace of many developers, ingraining its name in the list of the hardest programming languages to learn. Ben Olmstead invented this language in 1998, and the first program took nearly two years to build so that you can appreciate the intricacy of this language. Malbolge's complexity stems mostly from (a) restricted instructions, (b) instruction substitution after execution, and (c) loadable data restrictions. To this end, coding in this language appears to be garbage or malfunctioning. As a matter of fact, many believe that Ben Olmstead never developed a single program in this language. However, Hisashi Lizawa proposed obfuscation in software protection by providing a programming guide in Malbolge. For example, unreadable programs are resistant to alteration. Assume Alice wishes to transmit a program (or binary code) to Bob, who will run the application. Even if it is an encrypted code, Bob needs authorization to decrypt it for it to be executed. As a result, Bob has the opportunity to change it. However, an unreadable program, written in the toughest programming languages to learn, layers the code and functions as encrypted data. Now, that is a significant advantage. Equally important, the language earned its name after the eighth circle of hell in Dante's Inferno, namely reserved for those guilty of fraud. To put it differently, Malbolge is "A programming language that came from HELL." Haskell You will fail to learn this toughest programming language at least 3 to 4 times before finally grasping it. But needless to say, it will ruin other programming languages for you! Haskell is a fully functional programming language. In imperative languages, you accomplish things by assigning tasks to the computer, which then performs them. It may change state while performing them. However, it is lazy. Unless explicitly instructed differently, Haskell will not perform functions or compute anything until compelled to offer you a result. This is consistent with referential transparency because it allows you to think of programs as a sequence of data transformations. INTERCAL Jim Lyon and Don Woods created INTERCAL, one of the hardest coding languages to learn, in 1972 as a satire of several computer languages. 'Compiler Language With No Pronounceable Acronym.' was the first moniker given to it. INTERCAL includes various features that aim to frustrate programmers. For example, it employs modifiers such as "PLEASE". The compiler may reject it if the word "PLEASE" is not used frequently in the code. The program is deemed 'insufficiently courteous' by the organization. On the other hand, if the modifier 'PLEASE' is used too many times, the compiler rejects the code as 'excessively courteous.' BrainF**k A Swiss physics student called Urban Müller developed Brainfuck, in 1993 as an attempt to design a language with the smallest feasible compiler. Müller got the idea for Brainfuck from FALSE, a "perverse" programming language created earlier that year by Wouter van Oortmerssen, a Dutch software developer currently working at Google, with the purpose of producing a confusing Turing-complete language with the smallest feasible compiler. While modern programming languages like C++ may require a 2.6 Mb compiler, FALSE's compiler was just 1,024 bytes or nearly 2,600 times less. Because of the language's extraordinary simplicity, Brainfuck's compiler ended up being an order of magnitude smaller than FALSE at just 240 bytes. Brainfuck comprises only eight commands:, >, +, -, [,], and,. Despite its simplicity, the Brainfuck vocabulary is one of the most perplexing and difficult programming languages available owing to the large amount of code required to run a basic program. When a programmer wishes to learn a new language, for example, one of the first programs they normally teach themselves to create in that language is to print the word "Hello, World!" on the computer screen. The code for this application in a standard programming language like Python looks like this: However, the same program implemented in Brainfuck might look something like this: ++++++++++[>+++++++>++++++++++>+++>+<<<<-] ++.>+.+++++++..+++.>++.<<+++++++++++++++.>.+++.------.--------.>+.>. COW The name gives hundreds, if not thousands, a wrong impression. We are not indicating a technology that will help you talk with a cow. Nor do we plan to fabricate a Google Translate interface to uncover the hidden meaning of “MOO.” We are talking about one of the world’s top 6 hardest coding languages to learn in 2023, i.e., COW. It came into being in early 2013 and has received little attention from the development community. Later, it built a reputation as the most complex programming language in the world. Brainfuck is a bad influence on the COW, yet developers playfully built it with Bovine in mind. Cow contains 12 instructions, four more than Brainfuck, and is Turing complete. All 12 instructions are just variants of the word "MOO." Source: Cow Commands Whitespace Chris Morris and Edwin Brady created Whitespace at the University of Durham. The language debuted as one of the hardest languages to learn programming on April 1, 2003. The term "Whitespace" comes from the fact that it only employs three white characters: space, tab, and newline. As a result, the source code of programs written in Whitespace is rendered invisible. To make the code visible, three characters are commonly used: S for space (Space), T for tabulation (Tab), and L for new line (Line feed). Non-whitespace characters are ignored by the Whitespace interpreter and are treated as code comments. For example, a sequence of spaces might represent a single command, and a tab followed by a space might represent a different command. Because whitespace characters are not used to separate keywords or identifiers, like traditional programming languages, the source code written in whitespace looks like plain text with no distinguishable programming language syntax. Here's some code to display 'Hello, World!' in Whitespace. Want To Learn Some Real Programming? Mainstream programming needs the user to think in a specific way. However, esolangs encourage the user to think in ways that are useless and time-consuming yet nevertheless logical enough to develop complicated software. Therefore, we recommend skipping the hardest programming languages to learn as breakfast in 2023. But if you want to capitalize on real programming to retain your customers in this smart era, of InvoZone to bridge the gap in your company's talent pool! hire dedicated developers Frequently Asked Questions Is C++ the hardest programming language? It is difficult to say whether C++ is the hardest programming language, as the difficulty level can vary depending on the person learning the language and prior experience with programming. However, C++ is generally considered a more difficult language to learn than other popular programming languages, such as Python or , due to its complex syntax and steep learning curve. JavaScript Which is harder, C or C++? C and C++ are considered relatively low-level, systems-level programming languages, and both have a reputation for being difficult to learn. C is considered to be a relatively simple and minimalist language. It's often used as an introductory language to programming and computer science and gives you just what you need to get the job done. On the other hand, C++ is a more complex and feature-rich language, which has a steeper learning curve than C and adds several additional features such as object-oriented programming (OOP) support, templates, and a standard template library (STL).

7 Hardest Programming Languages to Learn in 2023

2023 সালে শেখার জন্য 7টি কঠিন প্রোগ্রামিং ভাষা

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2023 में सीखने के लिए 7 सबसे कठिन प्रोग्रामिंग भाषाएँ

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A Deep Dive Into Amdahl’s Law and Gustafson’s Law

আমডাহলের আইন এবং গুস্তাফসনের আইনের গভীরে ডুব দেওয়া

Ein tiefer Einblick in das Gesetz von Amdahl und Gustafson

Una inmersión profunda en la ley de Amdahl y la ley de Gustafson

Une plongée approfondie dans la loi d'Amdahl et la loi de Gustafson

अमदहल के नियम और गुस्ताफसन के नियम की गहन जानकारी

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Um mergulho profundo na lei de Amdahl e na lei de Gustafson

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深入研究阿姆达尔定律和古斯塔夫森定律

The Warden by Anthony Trollope, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Mr Bold's Visit to Plumstead here Mr Bold's Visit to Plumstead Whether or no the ill-natured prediction made by certain ladies in the beginning of the last chapter was or was not carried out to the letter, I am not in a position to state. Eleanor, however, certainly did feel herself to have been baffled as she returned home with all her news to her father. Certainly she had been victorious, certainly she had achieved her object, certainly she was not unhappy, and yet she did not feel herself triumphant. Everything would run smooth now. Eleanor was not at all addicted to the Lydian school of romance; she by no means objected to her lover because he came in at the door under the name of Absolute, instead of pulling her out of a window under the name of Beverley; and yet she felt that she had been imposed upon, and could hardly think of Mary Bold with sisterly charity. "I did think I could have trusted Mary," she said to herself over and over again. "Oh that she should have dared to keep me in the room when I tried to get out!" Eleanor, however, felt that the game was up, and that she had now nothing further to do but to add to the budget of news which was prepared for her father, that John Bold was her accepted lover. We will, however, now leave her on her way, and go with John Bold to Plumstead Episcopi, merely premising that Eleanor on reaching home will not find things so smooth as she fondly expected; two messengers had come, one to her father and the other to the archdeacon, and each of them much opposed to her quiet mode of solving all their difficulties; the one in the shape of a number of The Jupiter, and the other in that of a further opinion from Sir Abraham Haphazard. John Bold got on his horse and rode off to Plumstead Episcopi; not briskly and with eager spur, as men do ride when self-satisfied with their own intentions; but slowly, modestly, thoughtfully, and somewhat in dread of the coming interview. Now and again he would recur to the scene which was just over, support himself by the remembrance of the silence that gives consent, and exult as a happy lover. But even this feeling was not without a shade of remorse. Had he not shown himself childishly weak thus to yield up the resolve of many hours of thought to the tears of a pretty girl? How was he to meet his lawyer? How was he to back out of a matter in which his name was already so publicly concerned? What, oh what! was he to say to Tom Towers? While meditating these painful things he reached the lodge leading up to the archdeacon's glebe, and for the first time in his life found himself within the sacred precincts. All the doctor's children were together on the slope of the lawn, close to the road, as Bold rode up to the hall door. They were there holding high debate on matters evidently of deep interest at Plumstead Episcopi, and the voices of the boys had been heard before the lodge gate was closed. Florinda and Grizzel, frightened at the sight of so well-known an enemy to the family, fled on the first appearance of the horseman, and ran in terror to their mother's arms; not for them was it, tender branches, to resent injuries, or as members of a church militant to put on armour against its enemies. But the boys stood their ground like heroes, and boldly demanded the business of the intruder. "Do you want to see anybody here, sir?" said Henry, with a defiant eye and a hostile tone, which plainly said that at any rate no one there wanted to see the person so addressed; and as he spoke he brandished aloft his garden water-pot, holding it by the spout, ready for the braining of anyone. "Henry," said Charles James slowly, and with a certain dignity of diction, "Mr Bold of course would not have come without wanting to see someone; if Mr Bold has a proper ground for wanting to see some person here, of course he has a right to come." But Samuel stepped lightly up to the horse's head, and offered his services. "Oh, Mr Bold," said he, "papa, I'm sure, will be glad to see you; I suppose you want to see papa. Shall I hold your horse for you? Oh what a very pretty horse!" and he turned his head and winked funnily at his brothers. "Papa has heard such good news about the old hospital to-day. We know you'll be glad to hear it, because you're such a friend of grandpapa Harding, and so much in love with Aunt Nelly!" "How d'ye do, lads?" said Bold, dismounting. "I want to see your father if he's at home." "Lads!" said Henry, turning on his heel and addressing himself to his brother, but loud enough to be heard by Bold; "lads, indeed! if we're lads, what does he call himself?" Charles James condescended to say nothing further, but cocked his hat with much precision, and left the visitor to the care of his youngest brother. Samuel stayed till the servant came, chatting and patting the horse; but as soon as Bold had disappeared through the front door, he stuck a switch under the animal's tail to make him kick if possible. The church reformer soon found himself tête-à-tête with the archdeacon in that same room, in that sanctum sanctorum of the rectory, to which we have already been introduced. As he entered he heard the click of a certain patent lock, but it struck him with no surprise; the worthy clergyman was no doubt hiding from eyes profane his last much-studied sermon; for the archdeacon, though he preached but seldom, was famous for his sermons. No room, Bold thought, could have been more becoming for a dignitary of the church; each wall was loaded with theology; over each separate bookcase was printed in small gold letters the names of those great divines whose works were ranged beneath: beginning from the early fathers in due chronological order, there were to be found the precious labours of the chosen servants of the church down to the last pamphlet written in opposition to the consecration of Dr Hampden; and raised above this were to be seen the busts of the greatest among the great: Chrysostom, St Augustine, Thomas à Becket, Cardinal Wolsey, Archbishop Laud, and Dr Philpotts. Every appliance that could make study pleasant and give ease to the overtoiled brain was there; chairs made to relieve each limb and muscle; reading-desks and writing-desks to suit every attitude; lamps and candles mechanically contrived to throw their light on any favoured spot, as the student might desire; a shoal of newspapers to amuse the few leisure moments which might be stolen from the labours of the day; and then from the window a view right through a bosky vista along which ran a broad green path from the rectory to the church,—at the end of which the tawny-tinted fine old tower was seen with all its variegated pinnacles and parapets. Few parish churches in England are in better repair, or better worth keeping so, than that at Plumstead Episcopi; and yet it is built in a faulty style: the body of the church is low,—so low, that the nearly flat leaden roof would be visible from the churchyard, were it not for the carved parapet with which it is surrounded. It is cruciform, though the transepts are irregular, one being larger than the other; and the tower is much too high in proportion to the church. But the colour of the building is perfect; it is that rich yellow gray which one finds nowhere but in the south and west of England, and which is so strong a characteristic of most of our old houses of Tudor architecture. The stone work also is beautiful; the mullions of the windows and the thick tracery of the Gothic workmanship is as rich as fancy can desire; and though in gazing on such a structure one knows by rule that the old priests who built it, built it wrong, one cannot bring oneself to wish that they should have made it other than it is. When Bold was ushered into the book-room, he found its owner standing with his back to the empty fire-place ready to receive him, and he could not but perceive that that expansive brow was elated with triumph, and that those full heavy lips bore more prominently than usual an appearance of arrogant success. "Well, Mr Bold," said he;—"well, what can I do for you? Very happy, I can assure you, to do anything for such a friend of my father-in-law." "I hope you'll excuse my calling, Dr Grantly." "Certainly, certainly," said the archdeacon; "I can assure you, no apology is necessary from Mr Bold;—only let me know what I can do for him." Dr Grantly was standing himself, and he did not ask Bold to sit, and therefore he had to tell his tale standing, leaning on the table, with his hat in his hand. He did, however, manage to tell it; and as the archdeacon never once interrupted him, or even encouraged him by a single word, he was not long in coming to the end of it. "And so, Mr Bold, I'm to understand, I believe, that you are desirous of abandoning this attack upon Mr Harding." "Oh, Dr Grantly, there has been no attack, I can assure you—" "Well, well, we won't quarrel about words; I should call it an attack;—most men would so call an endeavour to take away from a man every shilling of income that he has to live upon; but it sha'n't be an attack, if you don't like it; you wish to abandon this—this little game of backgammon you've begun to play." "I intend to put an end to the legal proceedings which I have commenced." "I understand," said the archdeacon. "You've already had enough of it; well, I can't say that I am surprised; carrying on a losing lawsuit where one has nothing to gain, but everything to pay, is not pleasant." Bold turned very red in the face. "You misinterpret my motives," said he; "but, however, that is of little consequence. I did not come to trouble you with my motives, but to tell you a matter of fact. Good-morning, Dr Grantly." "One moment,—one moment," said the other. "I don't exactly appreciate the taste which induced you to make any personal communication to me on the subject; but I dare say I'm wrong, I dare say your judgment is the better of the two; but as you have done me the honour,—as you have, as it were, forced me into a certain amount of conversation on a subject which had better, perhaps, have been left to our lawyers, you will excuse me if I ask you to hear my reply to your communication." "I am in no hurry, Dr Grantly." "Well, I am, Mr Bold; my time is not exactly leisure time, and, therefore, if you please, we'll go to the point at once:—you're going to abandon this lawsuit?"—and he paused for a reply. "Yes, Dr Grantly, I am." "Having exposed a gentleman who was one of your father's warmest friends to all the ignominy and insolence which the press could heap upon his name, having somewhat ostentatiously declared that it was your duty as a man of high public virtue to protect those poor old fools whom you have humbugged there at the hospital, you now find that the game costs more than it's worth, and so you make up your mind to have done with it. A prudent resolution, Mr Bold; but it is a pity you should have been so long coming to it. Has it struck you that we may not now choose to give over? that we may find it necessary to punish the injury you have done to us? Are you aware, sir, that we have gone to enormous expense to resist this iniquitous attempt of yours?" Bold's face was now furiously red, and he nearly crushed his hat between his hands; but he said nothing. "We have found it necessary to employ the best advice that money could procure. Are you aware, sir, what may be the probable cost of securing the services of the attorney-general?" "Not in the least, Dr Grantly." "I dare say not, sir. When you recklessly put this affair into the hands of your friend Mr Finney, whose six-and-eightpences and thirteen-and-fourpences may, probably, not amount to a large sum, you were indifferent as to the cost and suffering which such a proceeding might entail on others; but are you aware, sir, that these crushing costs must now come out of your own pocket?" "Any demand of such a nature which Mr Harding's lawyer may have to make will doubtless be made to my lawyer." "'Mr Harding's lawyer and my lawyer!' Did you come here merely to refer me to the lawyers? Upon my word I think the honour of your visit might have been spared! And now, sir, I'll tell you what my opinion is:—my opinion is, that we shall not allow you to withdraw this matter from the courts." "You can do as you please, Dr Grantly; good-morning." "Hear me out, sir," said the archdeacon; "I have here in my hands the last opinion given in this matter by Sir Abraham Haphazard. I dare say you have already heard of this;—I dare say it has had something to do with your visit here to-day." "I know nothing whatever of Sir Abraham Haphazard or his opinion." "Be that as it may, here it is; he declares most explicitly that under no phasis of the affair whatever have you a leg to stand upon; that Mr Harding is as safe in his hospital as I am here in my rectory; that a more futile attempt to destroy a man was never made, than this which you have made to ruin Mr Harding. Here," and he slapped the paper on the table, "I have this opinion from the very first lawyer in the land; and under these circumstances you expect me to make you a low bow for your kind offer to release Mr Harding from the toils of your net! Sir, your net is not strong enough to hold him; sir, your net has fallen to pieces, and you knew that well enough before I told you;—and now, sir, I'll wish you good-morning, for I'm busy." Bold was now choking with passion. He had let the archdeacon run on because he knew not with what words to interrupt him; but now that he had been so defied and insulted, he could not leave the room without some reply. "Dr Grantly," he commenced. "I have nothing further to say or to hear," said the archdeacon. "I'll do myself the honour to order your horse." And he rang the bell. "I came here, Dr Grantly, with the warmest, kindest feelings—" "Oh, of course you did; nobody doubts it." "With the kindest feelings;—and they have been most grossly outraged by your treatment." "Of course they have;—I have not chosen to see my father-in-law ruined; what an outrage that has been to your feelings!" "The time will come, Dr Grantly, when you will understand why I called upon you to-day." "No doubt, no doubt. Is Mr Bold's horse there? That's right; open the front door. Good-morning, Mr Bold;" and the doctor stalked into his own drawing-room, closing the door behind him, and making it quite impossible that John Bold should speak another word. As he got on his horse, which he was fain to do feeling like a dog turned out of a kitchen, he was again greeted by little Sammy. "Good-bye, Mr Bold; I hope we may have the pleasure of seeing you again before long; I am sure papa will always be glad to see you." That was certainly the bitterest moment in John Bold's life. Not even the remembrance of his successful love could comfort him; nay, when he thought of Eleanor he felt that it was that very love which had brought him to such a pass. That he should have been so insulted, and be unable to reply! That he should have given up so much to the request of a girl, and then have had his motives so misunderstood! That he should have made so gross a mistake as this visit of his to the archdeacon's! He bit the top of his whip, till he penetrated the horn of which it was made: he struck the poor animal in his anger, and then was doubly angry with himself at his futile passion. He had been so completely checkmated, so palpably overcome! and what was he to do? He could not continue his action after pledging himself to abandon it; nor was there any revenge in that;—it was the very step to which his enemy had endeavoured to goad him! He threw the reins to the servant who came to take his horse, and rushed upstairs into his drawing-room, where his sister Mary was sitting. "If there be a devil," said he, "a real devil here on earth, it is Dr Grantly." He vouchsafed her no further intelligence, but again seizing his hat, he rushed out, and took his departure for London without another word to anyone. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anthony Trollope (1996). The Warden. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/619/pg619-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Mr Bold's Visit to Plumstead

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