Scott Dietzen, CEO of Augment Code, discusses how artificial intelligence (AI) is reshaping large-scale software engineering. He shares his background in machine learning and his experience working with massive, complex codebases, highlighting the challenges engineers face when maintaining and updating them. Then he explains how AI can help developers navigate these challenges by understanding the structure of their code, reducing errors, and making large projects more manageable.
Google today previewed a set of extensions to its artificial intelligence (AI) coding tools that are designed to be integrated directly within a development environment.
Based on the Gemini 2.0 Flash large language model that Google previewed last week, the Gemini Code Assist tools that are being added are designed to make it simpler for developers to both invoke and retrieve information from external sources.
Google Code Assist launch partners include Atlassian, GitHub, GitLab, Google Docs, Sentry.io and Snyk. Additional partners signaling their support for Gemini Code Assist tools include Dynatrace, New Relic, Black Duck, Harness, Aiven, DataStax, Elastic, MongoDB, Neo4j, Redis and SingleStore.
Those integrations, for example, will make it simpler to summarize recent comments from a Jira issue, find the last person who merged changes to a file in a Git repository or identify the most recent live site issue.
Any natural language command can be converted into a parametrized call to an application programming interface as defined by the OpenAPI standard or using a YAML file provided by a DevOps team. Those calls are then secured using the OAuth authentication framework.
Mitch Ashley, vice president and practice lead for DevOps and application development for The Futurum Group, said while much of the early AI focus has been on using these tools to generate code, most of the real productivity benefits will come from the ability to automate adjacent tasks, such as writing test plans, explaining code and verifying fixes have been properly installed.
A Techstrong Research survey finds a third (33%) are working for organizations that already make use of AI to build software, while another 42% are considering it. However, the survey also finds only 9% have fully integrated AI into their DevOps pipelines, while another 22% have partially achieved that goal. A total of 28% said they expect to integrate AI into their workflows in the next 12 months.
The survey makes it clear that in the coming year, the focus needs to shift well beyond simply making individual developers more productive, said Ashley.
Each organization will naturally adopt AI as they best see fit, but it’s no longer a question of whether to use AI as much as to what degree to rely on it. While AI coding tools make developers more productive, organizations need to make sure the code being created is thoroughly reviewed before incorporating it into a production environment. AI platforms were trained using samples of code of varying quality collected from across the web. In some instances, the code generated might therefore include a vulnerability that could be easily exploited. Conversely, depending on the skills of the developer, the code being generated might just as easily be of a higher quality than what might otherwise be created.
Regardless of approach, the one certain thing is developers will become more productive. Less clear, however, is whether that increase in productivity for individual developers will actually result in more higher-quality applications being built and deployed in production environments faster.
Google today previewed an experimental edition of the Jules coding agent, that makes use of next-generation multimodal Gemini 2.0 models to automate coding tasks.
Designed to be integrated with code repositories such as GitHub, Julles is capable of creating and executing multi-step plans and processes, including modifying multiple files, preparing pull requests and fixing bugs in software.
Available today for a select group of Python and Javascript developers, Google plans to make Jules more widely available to developers early next year as its Gemini 2.0 models become more widely available.
At the same time, Google also revealed that Colab is updating its AI agent for automating data science tasks to add support for Gemini 2.0 models.
Those next-generation Gemini 2.0 multimodal models can now be invoked via a single Multimodal Live application programming interface (API) that Google is also launching. The first in that series is a Gemini 2.0 Flash model that makes it simpler for development teams to mix and match video, voice, spatial understanding and text within their applications in real-time using a single API.
Google AI Studio tools and the Vertex AI service provided by Google will be able to invoke that API to build those applications, with general availability of that integration scheduled for early 2025. Google is also making available three starter application experiences in Google AI Studio that can be customized along with open-source code for spatial understanding, video analysis and Google Maps exploration.
Developers can also use Gemini 2.0 Flash in Gemini Code Assist to embed this capability into their integrated development environments (IDEs). Gemini 2.0 AI models will in the months ahead also be embedded in Android Studio, Chrome DevTools and Firebase in the coming months.
The overall goal is to make it simpler for application developers will also be able to invoke native text-to-speech audio output depending on the quality required across eight different options. Gemini 2.0 Flash also natively generates images and supports conversational, multi-turn editing to enable developers to build on previous outputs they may have created,
Additionally, application developers will be able to use Gemini 2.0 to build AI agents to automate a wide range of tasks in addition to taking advantage of a Project Mariner initiative that will integrate AI agents with browsers
Logan Kilpatrick, a senior product manager for Google, said the overall goal is to make it simpler to build multimodal AI applications using Gemini 2.0 models that, based on industry benchmarks, will be twice as fast as the previous generation of Gemini 1.5 models.
It’s not clear to what degree AI agents will transform DevOps workflows, but interest in applying generative AI to software engineering is clearly running high. A Techstrong Research survey finds a third (33%) of DevOps practitioners are working for organizations that make use of artificial intelligence (AI) to build software, while another 42% are considering it. However, only 9% have fully integrated AI into their DevOps pipelines. Another 22% have partially achieved that goal, while 14% are doing so only for new projects. A total of 28% said they expect to integrate AI into their workflows in the next 12 months.
There will, of course, soon be no shortage of options for employing AI agents across DevOps workflows. The challenge now is determining what task to allocate to those AI agents in a way that ultimately increases the actual pace at which more applications are built and deployed.
GitHub today revealed it is adding support for both the Claude Sonnet 3.5 large language model (LLM) from Anthropic and the Gemini Pro 1.5 LLM from Google to its artificial intelligence (AI) platform for writing code.
In addition, the company has made available a technical preview of GitHub Spark, an application development tool that leverages generative AI to enable applications to be built in plain language rather than, for example, using Python or JavaScript.
Announced at the GitHub Universe 2024 conference, GitHub is now making a concerted effort to ensure application development teams that have adopted GitHub Copilot are not locked into any one specific LLM. Previously, GitHub Copilot only provided access to the LLMs provided by Open AI, which GitHub Copilot has been updated to add support for the latest o1 Preview and o1 Mini LLMs.
GitHub CEO Thomas Dohmke told conference attendees that it can no longer be denied that open-source software has won. At the same time, AI application development is evolving beyond the current AI infusion stage to an AI native era where capabilities are not added to tools and platforms but are instead deeply embedded in a way that can’t be separated.
Conversational AI tools will also be supplanted by AI agents that have been trained to automate specific tasks, he added. For example, As part of that effort, GitHub has added two AI agents for ideation and code repair to Copilot Workspace, a platform for automating the development of software engineering workflows that are still being tested. In total, GitHub has now developed five AI agents, including one that automates upgrades of Java runtimes, In addition, GitHub now provides an extension that integrates those agents with the VS Code integrated development environment (IDE).
Finally, GitHub revealed that GitHub Models, a service through which GitHub makes multiple AI models available to developers, is now generally available.
Overall, there are now more than 137,000 public generative AI projects hosted on GitHub, with Python now having surpassed JavaScript as the most widely used programming language on the platform. It’s not clear how long that might be the case once tools such as GitHub Sparks become generally available to a pool of developers that could soon number in the billions. In effect, GitHub Sparks eliminates the need to master a programming language to build an application, noted Dohmke.
AI tools are already widely employed by many DevOps teams. A Techstrong Research survey found a third (33%) are working for organizations that make use of artificial intelligence (AI) to build software, while another 42% are considering it. Only 6% said they have no plans to use AI. However, only 9% have fully integrated AI into their DevOps pipelines. Another 22% have partially achieved that goal, while 14% are doing so only for new projects. A total of 28% said they expect to integrate AI into their workflows in the next 12 months.
Mitch Ashley, vice president and practice lead of DevOps and application development for The Futurum Group, said as a widely used platform for software development and innovation, GitHub is uniquely positioned to increase uses of AI in software development. In 2025, we will also see the introduction of smaller language models that begin to specialize, solving vexing problems in software, such as recommending vulnerability remediation, applying software patches and upgrades, and more secure and higher quality code, he added.
However, the usage of AI does not automatically lead to increased productivity. In fact, a recent DORA report suggests that in some instances the rate at which software is being deployed has slowed since the rise of AI. One way or another, however, software engineering in the age of AI will never be the same again.
A survey of 406 developers, software engineers, CTOs, CISOs and application security professionals finds that while the majority of respondents (80%) said their organization is ready to use artificial intelligence (AI) tools to write code, the level of enthusiasm for these tools is far higher among C-level executives.
In fact, 40% of the 135 C-level executives surveyed said their organization is “extremely ready” compared to only 26% of 58 application security professionals and 22% of the 119 developers surveyed. A third of the C-level executives said adopting AI coding tools is critical for their organization, with 19% noting they did not attribute any risk to AI coding tools.
Snyk CTO Danny Allan said, in general, organizations would be well advised to test the output of generative AI coding tools within the context of their use cases. Generative AI tools are probabilistic rather than deterministic, so the quality of the output can vary considerably. Unfortunately, there is a natural tendency among developers to place too much faith in the output of an AI coding tool, he noted.
Nevertheless, roughly two-thirds of respondents (63%) rated the security of AI-generated code as either “excellent or “good.” Only 6% rated it as “bad”. At the same time, however, well under half said their organizations provided AI coding tool training to the majority of their developers, the survey finds.
In fact, the survey suggests that not many organizations are pursuing a deliberate approach to AI coding. Less than 20% of respondents said their organization undertook a proof-of-concept (POC) as part of their preparation steps for adopting AI coding tools.
It’s still early days so far as the adoption of AI coding tools is concerned, but usage is probably much broader than officially recognized. Individual developers may not disclose when they are using these tools to help them write code. DevOps teams should be making extensive use of software bill of materials (SBOMs) to help identify which code was written by who, or in the case of machines, what, noted Allan.
Of course, there will probably soon come a day when organizations are using AI to govern how developers are using AI tools to write code, but in the meantime DevOps teams should assume they will need to troubleshoot a fair amount of the code being generated by machines. Less clear is whether that code will be any better or worse than what is created by human developers but undoubtedly there will, as developers become more productive, be a lot more code soon finding its way into production environments.
Ultimately, AI will also be infused into the tools that DevOps teams rely on to manage code, but in the short term there may be an imbalance. Developers are gaining access to AI tools faster than DevOps engineers that generally need to wait for the next round of updates to the platforms they rely on to manage software engineering workflows.
Regardless of how all the code moving through DevOps pipelines was created, however, the one thing that won’t change is the degree to which software engineers will be held accountable for it.
Anima today revealed is has added an ability to personalize the code its generative artificial intelligence (AI) platform creates based on how an application is described using tools from Figma and Adobe.
That capability makes it possible for the code being generated to resemble the coding style of an organization or specific developer more closely.
Anima CEO Avishay Cohen said the company developed this capability to reduce the editing time that is otherwise required when trying to embed code generated by a large language model (LLM) within an application that has a well-defined coding methodology.
The platform that Anima provides is based on a mix of LLMs and heuristics that the company developed to generate code. In this case, the code being generated is written in React or HTML to make it simpler to build an application’s front end. In contrast, a general-purpose LLM such as ChatGPT is not optimized for creating these types of graphical applications.
Developers no longer need to worry about, for example, making sure each pixel is perfectly placed because the Anima platform automates that part of the application development process, including adding responsive behavior, animations and logic, noted Cohen. In addition, customized instructions may be applied to an entire project or even a specific component.
In effect, the platform enables backend developers to automatically create a front-end for their application from a description in tools provided by Figma or Adobe. As a result, developers can allocate more time to writing business logic and optimizing application performance, said Cohen.
Later this year, Anima will integrate its platform with GitHub and other continuous integration/continuous delivery (CI/CD) platforms as part of an effort to extend the reach of the platform across an entire codebase, he added.
There is, of course, already no shortage of generative AI tools for writing code, but the degree to which they have been incorporated into DevOps workflows varies from one organization to the next. Much of the code created using platforms such as ChatGPT can vary widely in terms of quality. These platforms were trained using code collected from across the web so it’s not uncommon for the code generated to include known vulnerabilities that DevOps teams need to scan before incorporating into a build. In other cases, the code generated might simply break when installed.
Regardless of these issues, however, the amount of code being generated by machines is only going to increase, especially as more LLMs that are specifically trained to create code based on examples that have been closely vetted become more widely available.
In the meantime, developers who never especially enjoyed building the visual front ends of applications can now focus more of their time on tasks they prefer. Alternatively, developers who specialize in the front ends of applications will find that much of the toil involved will soon be automated in a way that should provide more time for them to create customized experiences.
One way or another, the overall pace at which applications are built is about to exponentially increase. The challenge now will be finding ways to take advantage of AI to enable DevOps teams to keep pace.
Quantum computing is poised to transform the way companies solve business problems. The technology needed to power this revolution is becoming a reality, with large developers like IBM making excellent progress toward building quantum-centric supercomputers. But while the technological barrier to deploying quantum is falling away, another obstacle remains: The lack of quantum skills in the labor market.
Last year, Classiq commissioned a survey of over 500 professionals familiar with quantum technology, which highlighted a range of possible benefits that quantum computing will unlock, including cost savings, new revenue streams and competitive advantages for companies that successfully deploy the technology. But the survey also highlighted that the shortage of qualified engineers and scientists who can program quantum computers remains one of the biggest barriers to increased quantum deployments.
This skills gap will not only prevent companies from leveraging the power of quantum computing but also lead to expensive competition for talent as businesses try to recruit the few quantum professionals available in the workforce. Why has this skills gap grown, and what can companies do about it?
First, we need to understand what skills are missing that are contributing to a skills gap, as some readers may ask why existing programmers and computer engineers cannot meet this demand. Besides lacking the purely technical skills needed to code for quantum computing, such as creating quantum circuits and software, there is also a knowledge gap around how to solve business problems using a quantum computer.
For instance, while many programmers trained in classical computing could figure out a way to design a program for portfolio optimization, they likely have no idea how to do the same using a quantum computer. They are unlikely to know where to start, how to check their results or even how to run their code on a quantum device.
Coding for quantum computers is also more complicated than for classical computing. As each “qubit” can be one and zero simultaneously instead of either/or, writing the quantum code is more complex. Developers need to understand quantum concepts such as superpositions, so working in quantum requires new ways of thinking about problems and necessitates new ways of working.
This last issue is compounded by a technology gap, as quantum programming has not been abstracted away as it has for classical programming – it is mostly still at the assembly language level. At this level, writing a few dozen lines of quantum programming is achievable but time-consuming, so creating complex quantum programs of hundreds or thousands of lines of code is almost impossible.
The first instinct for companies wishing to leverage quantum technology is to hire their way out of this problem, but there are simply not enough Ph.D.-trained quantum experts in the market, and there is likely to be stiff competition for the few quantum-trained graduates coming out of university. Given the shortage, companies must instead consider how to train and upskill their staff.
Convincing existing staff to take part in this training should not be an issue. Engineers and programmers are keen to learn – nearly 95% of respondents to our survey said they would like to be trained in quantum. Why is there such high demand? Not only is this new technology exciting, but many workers may hope to future-proof their jobs or increase their earning potential through quantum skills.
Upskilling your workforce does not have to be expensive. There is already a wide range of open source material, such as the Qiskit development kit, designed to help people explore this new technology. For companies, a straightforward solution is to give their engineers the time and space to learn about quantum for themselves. Simply giving your existing coders permission and resources to learn about this technology and how it can benefit your business could prove to be a profitable long-term investment.
The added advantage of upskilling your staff, rather than recruiting quantum Ph.D. graduates, is your existing workforce understands the sorts of problems your business needs to solve.
Plus, new products are emerging that can help solve the technology gap and make it easier to code for the quantum era. Sophisticated low-code software can abstract away some of the complexity involved in writing quantum coding, which in turn will speed up the process of learning.
But upskilling is only one thing to consider. Companies also need to invest in the resources and infrastructure around their emerging quantum talent. They need to build a team, identify use cases that can benefit from quantum technology and locate the right vendors and cloud providers that will allow them to test and simulate their quantum programs.
The quantum revolution remains several years away, but companies need to make sure they have the skills and infrastructure ready to take advantage of this technology or risk being left behind by quantum-prepared competitors.
As we navigate another year of consistent zero-day breaches, legislative pivots, the explosion of AI tooling and threat actors growing bolder and more desperate, it’s safe to say that getting comfortable with change is a requirement for thriving in the technology industry.
We occupy a notoriously unpredictable space, but that’s half the fun. Compared to many other verticals, technology—especially cybersecurity—is relatively youthful, and the future should be something we can all look forward to blossoming in sophistication alongside the technology we swear to protect.
So, what can we expect in the industry in 2024? We put our heads together, looked into our crystal ball, and these were the results:
It was the talk of the conference circuit in 2023, with several high-profile presentations at Black Hat, DEF CON, Infosecurity Europe and many more warning of the explosive changes we can expect from AI implementation across every industry, especially cybersecurity. As tends to happen with low barriers to entry for such transformative technology, adoption has outpaced any official regulation or mandates at the government level.
With significant movements in general cybersecurity guidelines and benchmarks around the world, including CISA’s Secure-by-Design and -Default principles in the U.S. and similar initiatives from the UK and Australian governments, it is essentially a foregone conclusion that regulations around AI use will be announced sooner rather than later.
While much of the debate surrounding the mainstream use of AI tooling and LLMs has centered around copyright issues with training data, another perspective delves into how AI is best used in cybersecurity practices. When it comes to coding, perhaps its most human quality is its similar hardship in displaying contextual security awareness, and this factor is deeply concerning as more developers are adopting AI coding assistants in the construction of software. This has not gone unnoticed, and in a time of increased scrutiny for software vendors adopting security best practices, government-level intervention certainly would not surprise.
Much has been written about the AI takeover, and for the better part of a year, we have been subject to a plethora of clickbait headlines that spell doom and destruction for just about every white-collar profession out there, and developers were not spared.
After months of speculation and experimentation with LLMs in a coding context, we remain entirely unconvinced that development jobs are at collective risk. There is no doubt that AI/ML coding tools represent a new era of powerful assistive technology for developers, but they are trained on human-created input and data, and that has rendered the results far from perfect. Perhaps if every developer on the planet was a top-tier, security-minded engineer, we might see genuine cause for concern.
However, just as the average adult driver vastly overshoots their ability (notice how everyone says they’re a great driver, and it’s always other people who lack skill? That’s a classic example of the Dunning-Kruger effect!), so too does the development community, especially when it comes to security best practices. According to one Stanford study into developer use of AI tooling, it is likely that unskilled developers using this technology will become dangerous. The study claimed that participants who had access to AI assistants were more likely to introduce security vulnerabilities for the majority of programming tasks yet also more likely to rate their insecure answers as secure. This poses a significant issue; poor developers will be enabled to introduce security issues faster, and if anything, this will only increase the need for security-skilled developers with the knowledge and expertise to code securely and use AI technology safely.
CISA director Jen Easterly has made it abundantly clear that software vendors should not be permitted to “pass the buck” when it comes to security within their products, highlighting that the current responsibility for software safety is largely passed to the consumer.
We have agreed with this view, and we believe it will take a shakeup of this magnitude to move the needle toward code-level security—not to mention appropriately educating developers—to be taken seriously.
While CISA’s powers only extend so far—essentially, they can only enforce secure-by-design practices to vendors that sell to federal agencies—this still presents a new security benchmark to hit for many large software vendors. Colonial Pipeline, SUNBURST and, more recently, the MOVEit data breach are all large-scale cyberattacks that affected government-level systems at some point, and with these new guidelines in play, there is a real possibility that future highly visible breaches will receive greater scrutiny and reprimand.
As the goal of increased cyber resilience continues to dominate cyber strategies across multiple verticals, those who rely on reaction and incident response as the only core tenets of their plan will find themselves in a place of unacceptable exposure and risk.
Security professionals must act swiftly in the face of adversity and outright attack, but modern times call for modern solutions, and we simply cannot afford to take a less-than-holistic approach. “Shift left” needs to be more than a rapidly aging buzzword; code-level security should be prioritized, alongside upskilling and verifying the competence of the developers working on the software and critical digital infrastructure we take for granted. After all, who wants the security program equivalent of an Etch-a-Sketch when they could have an iPad Pro?
Now, more than ever, governments and enterprises alike must commit themselves to a preventative, high-awareness security program in which every member of staff is enabled to share responsibility. It’s not enough to cite the cybersecurity skills gap as a reason for falling behind; investment in security-aware developers and fostering collaboration between them and their AppSec counterparts should be a driving force in remaining as secure as possible, both as an organization and within the production of software.
Matias Madou, co-founder and CTO at Secure Code Warrior, contributed to this article.
In the tech world, hackathons are not just events; they’re incubators for real-world innovation and collaboration. They bring together not just coders, but visionaries eager to push the boundaries of what’s possible with technology. As these events become mainstream, organizations from tech giants to startups are leveraging hackathons not just to unearth coding prowess, but to kindle a culture of continuous innovation and ethical AI development.
If it sounds like a hackathon would benefit your organization, you’re likely correct – but a challenging, fun and well-organized hackathon doesn’t happen on a whim. First, you need to nail down ground rules, guardrails and goals to ensure the best return from the event.
Before determining the objective or challenge of the hackathon, it’s important to consider your organization’s priorities and what you hope to achieve by hosting it. This can – and should – vary widely depending on the scope of your business.
If you’re a vendor of proprietary software, you might consider using the hackathon to gauge the accessibility and user experience offered through your project. It could also be an opportunity to encourage customers and partners to use your solutions to solve business problems in ways they hadn’t thought of before. If you’re questioning current cybersecurity measures, it could be beneficial to format the event around auditing and recording security discrepancies or vulnerabilities.
Hackathons also don’t need to be an externally facing event – if you have in-house talent, hosting an internal hackathon could make the most of your resources while bolstering camaraderie and chemistry among employees. Internal hackathons can enable goals in sensitive areas of your business that couldn’t be exposed to external developers.
Once you’ve determined the primary objective and attendee profile for your hackathon, you can determine the challenge you’ll pose to participants that will ultimately yield the desired outcome.
The crux of a successful hackathon is a challenge that mirrors real business problems, engaging minds to develop solutions that are not only innovative but also practical and ethical. It’s about responsibly applying AI to do good in ways that resonate with your core values. At ABBYY, for instance, we’ve witnessed teams of professionals harness the low-code capabilities of our intelligent document processing platform to solve complex data puzzles within hours — not just as a testament to the power of AI but to the creativity and collaborative spirit of the participants.
These teams were comprised of professionals from Deloitte, PWC and Unilever, indicating a strong knowledge base of document-centric challenges. They worked to solve advanced scenarios involving handwritten forms with identity documents, claims processing, bank checks with signature extraction, unstructured data with named entity recognition, combined PDF, and bank statements.
With the vast experience of the competing teams and the accessible low-code/no-code interface of Vantage, the winners of the hackathon were able to develop and present robust solutions within a four-hour period.
Note the designation of “winners,” as hackathons are traditionally a competitive activity. Therefore, there needs to be guidelines for rules and adjudication while still ensuring creative freedom.
Competition, while central to the hackathon’s vigor, is curated to fuel creativity and not just to crown victors. It’s about recognizing those who think differently and championing the potential of AI to enhance — not replace — the human element in the workplace.
Hackathons often entail both development and presentation skills, as teams will pitch their completed solution. Judging criteria should consider all steps of this process and account for the competitors’ ability to illustrate the full breadth of their product. This ensures that all team members’ efforts are considered, encouraging deeper collaboration.
An example of comprehensive judging criteria could be:
• Completeness of solutions
• Functionality of solutions
• Uniqueness of solutions
• Clarity of presentations
• Teamwork and collaboration
Not every team will walk away a winner, but with a sufficiently engaging objective, each participant can leave the hackathon having learned something new, made a new connection, or otherwise developed themselves and their professional skills.
Feedback from these events is gold dust. It’s not just about what went right; it’s about learning, adapting and constantly improving. It’s a dialogue that continues long after the hackathon ends, ensuring that each subsequent event is more aligned with your vision for AI’s role in society.
Were there opportunities for logistical and organizational improvement that could have limited the event’s potential? Were the assigned scenarios excessively challenging, or perhaps not challenging enough? Did participants expect more opportunities for networking? Ask your attendees for their thoughts on every facet of the event that could have impacted the value brought to them and your organization.
Participants in ABBYY’s recent hackathon found value across the spectrum of development and professional growth. “I feel like we learned many things, like executing new strategies and how we can interact with other technologies,” said Radhakrishnan R, associate software engineer at PWC. “The other great thing is we connected with many people. This was a good opportunity to build a great network – we saw how many people are working with this technology, and we got to meet them in person.”
For companies that sit at the forefront of AI technology, hackathons are an opportunity to test the waters of user experience and accessibility and to push platforms to solve business problems creatively and ethically. They are a testament to a commitment to compliant and explainable AI, aligning with the ethos that technology must be as responsible as it is revolutionary.
In essence, a hackathon, when done right, reflects a belief in the symbiotic relationship between humans and AI. It’s a platform where innovation meets practicality, where ethical AI development is not just a buzzword but a practice, and where every participant, regardless of the outcome, leaves with a richer understanding of AI’s transformative power.
