By removing the preconceived notions about how challenging programming is, Jason Laster became more confident in building a developer-friendly debug tool. “We want to make software more approachable,” he said. “We want more people to feel like they can program and do things that don’t require a math degree.” He went on to say, “Imagine being a Project Manager and asking your engineer why something broke and receiving a long explanation that still leaves your question unanswered. Using Replay, they can share the URL with the engineers who can just go in and leave a comment. Now, the PM can recognize the function and identify what went wrong on their own. If anybody along the way can record the issue with Replay, then everyone downstream can look at the replay, debug it and see exactly what went wrong.” Acknowledging that it’s easy to mistake Replay as another browser recorder tool, Laster explained how Replay differs. “On one end of the spectrum, you have something like a video recorder, then go along that spectrum a little bit further and you have something like a session replay tool and observability tool.
The increasing diversity of AI workloads has necessitated a business demand for a variety of AI-optimized hardware architectures. These can be classified into three main categories: AI-accelerated CPU, AI-accelerated GPU, and dedicated hardware AI accelerators. We see multiple examples of all three of these hardware categories in the market today, for example Intel Xeon CPUs with DL Boost, Apple CPUs with Neural Engine, Nvidia GPUs with tensor cores, Google TPUs, AWS Inferentia, Habana Gaudi and many others that are under development by a combination of traditional hardware companies, cloud service providers, and AI startups. While AI hardware has continued to take tremendous strides, the growth rate of AI model complexity far outstrips hardware advancements. About three years ago, a Natural Language AI model like ELMo had ‘just’ 94 million parameters whereas this year, the largest models reached over 1 trillion parameters.
Many companies are extremely hesitant about introducing the Industrial Internet of Things (IIoT) or cloud systems because they believe it will open the door to cybercriminals. What they fail to realize is they’re already facing this danger every day. A simple email with an attachment or a link can result in the encryption of all the information on a server. You’re at risk even if you haven’t implemented an entire ecosystem connecting customers and suppliers. That’s why it’s essential that you’re aware of the threats and be ready to respond quickly in the event of a cyberattack. Cybersecurity is currently on everyone’s lips. In many widely publicized cases, large companies have fallen victim to cyberattacks that compromised their operations in one way or another. In some of these cases, the companies’ security policies had not kept up with the past decade’s rapid changes in the use of digital technologies and tools. They mistakenly thought a cyberattack could only affect others. The sheet metal processing sector is no exception to this reality.
Monitoring and observability have become one of the most essential capabilities for engineering teams and in general for modern digital enterprises who want to deliver excellence in their solutions. Since there are many reasons to monitor and observe the systems, Google has documented Four Golden Signals or metrics that define what it means for the system to be healthy and that are the foundation for the current state of the observability and monitoring platforms. The four metrics are described below: Latency is the time that a service takes to service a request. It includes HTTP 500 errors triggered due to loss of connection to a database or other critical backend that might not be served very quickly. Latency is a basic metric since a slow error is even worse than a fast error. Traffic is a measure of how much demand is being placed on the system. It determines how much stress is the system taking at a given time from users or transactions running through the service. For a web service, for example, this measurement is usually HTTP requests per second.
As the world emerges from the impact of the pandemic, hyperautomation solutions will power digital self-services to take center stage connecting businesses with customers. With customers opening bank accounts remotely, consulting doctors online, interacting with governments via citizen self-serve, and so on, the scope of tech-enabled services keeps expanding from time to time. All this implies that there will be a gradual shift away from the traditional back-office towards self-serve. From a hyperautomation standpoint, this shift will see a considerable boost from low-code platforms with favorable B2C type interactions. Rich and sophisticated user experiences centered around simplicity and ease of use will be in demand. New user experiences will break ground allowing more flexibility and improved speed-to-solution. In addition to B2C type low-code portals, Artificial Intelligence (AI) and analytics will be in demand. For example, organizations will deploy AI technologies heavily to assist customer interactions.
On the benefits and harms of algorithms, the DRCF identified “six cross-cutting focus areas” for its work going forward: transparency of processing; fairness for those affected; access to information products, services and rights; resilience of infrastructure and systems; individual autonomy for informed decision-making; and healthy competition to promote innovation and better consumer outcomes. On algorithmic auditing, the DRCF said the stakeholders pointed to a number of issues in the current landscape: “First, they suggested that there is lack of effective governance in the auditing ecosystem, including a lack of clarity around the standards that auditors should be auditing against and around what good auditing and outcomes look like. “Second, they told us that it was difficult for some auditors, such as academics or civil society bodies, to access algorithmic systems to scrutinise them effectively. Third, they highlighted that there were insufficient avenues for those impacted by algorithmic processing to seek redress, and that it was important for regulators to ensure action is taken to remedy harms that have been surfaced by audits.”
“It is natural to see providers making it easier for developers to do those things and that is where we get into infrastructure meeting software development,” RedMonk analyst James Governor told InfoWorld. “At the end of the day, you need platforms to enable you to be more productive without manually dealing with Helm charts, operators, or YAML.” Improving the back-end developer experience can do more than improve the lives of back-end developers. Providing better, more intuitive tools can enable back-end developers to get more done, while also bringing down barriers to allow a wider cohort of developers to manage their own infrastructure through thoughtful abstractions. “Developer control over infrastructure isn’t an all-or-nothing proposition,” Gartner analyst Lydia Leong wrote. “Responsibility can be divided across the application lifecycle, so that you can get benefits from “you build it, you run it” without necessarily parachuting your developers into an untamed and unknown wilderness and wishing them luck in surviving because it’s not an ‘infrastructure and operations team problem’ anymore.”
Before jumping the gun, identify your bottlenecks, understand the delivery systems available and discover the root cause of the congestion. Factors to analyze are the capacity of your shipping mediums, your warehouse management, average delivery time and the accuracy of your demand predictions. Only by understanding your current capabilities and inefficiencies will you be able to deploy the appropriate technology. Build your systems in an orderly manner: Build out your technology step by step. This is vital since some companies assume that adding multiple solutions and automating everything at once will reap the best results. This is not the case. ... Overall, applying AI analytics to problems will help you optimize elements like your optimal warehouse capacity, transportation utilization and delivery times. At some point, however, business leaders have to choose between tradeoffs. Is the main goal to keep costs low or to increase delivery speed? Are long transport distances to be avoided due to emissions? While AI can show which alternatives are more cost-effective or climate-friendly, companies will have to make the ultimate decision about their business trajectory.
When an asset is under attack, security analysts need to understand if it is a test/development server or a cloud-based workload hosting a business-critical application. To get this perspective, SOC modernization combines threat, vulnerability, and business context data for analysts. ... Cisco purchased Kenna Security for risk-based vulnerability management, Mandiant grabbed Intrigue for attack surface management, and Palo Alto gobbled up Expanse Networks for ASM as well. Meanwhile, SIEM leader Splunk provides risk-based alerting to help analysts prioritize response and remediation actions. SOC modernization makes this blend a requirement. ... SOC modernization includes a commitment to constant improvement. This means understanding threat actor behavior, validating that security defenses can counteract modern attacks, and then reinforcing any defensive gaps that arise. CISOs are moving toward continuous red teaming and purple teaming for this very purpose. In this way, SOC modernization will drive demand for continuous testing and attack path management tools from vendors like AttackIQ, Cymulate, Randori, SafeBreach, and XMCyber.
Experience counts a lot for developers, just as it does for pilots or surgeons. Technical experience is relatively easy to pick up, but the experiences that build instinct in the best developers are rarely gained alone. Developers work with others and learn from one another along the way. They seek collaboration on difficult problems and offer thoughtful feedback and suggestions on work in progress. Ultimately, developer tools are built for collaboration, encouraging the exchange of comments and open discussion. There are so many misconceptions about successful developers. Some of them may have some truth to them, while others are outdated or were completely false in the first place. The idea of developers as antisocial individuals is not always accurate. Developers are more often creative problem solvers who combine creativity with deep skills to tackle the task at hand. The most successful developers combine emotional intelligence with hard work and a curiosity for learning something new – and they help others around them to do the same.
Quote for the day:
"Without courage, it doesn't matter how good the leader's intentions are." -- Orrin Woodward