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OpenAI and Deeplearning.ai have collaborated to create a new course on ChatGPT prompt engineering for developers. The course focuses on teaching developers how to build applications using API access to large language models (LLMs). It covers principles for prompting, common use cases such as summarizing, inferring, transforming, and expanding text. Additionally, learners will learn how to build a custom chatbot using a language model. The goal is to inspire learners to explore new applications that can be easily built using language models and effective prompting. By the end of the course, learners will have a good understanding of building applications on large language models and hopefully gain new ideas for their own projects.

In this video, we explore a world where presentations and artificial intelligence come together. To use this technology, simply input the topic or title of your presentation and let Degtypos do the thinking. You can also choose your goal for the presentation to optimize the suggested content. With this tool, you'll have a first draft to start working with.

Amy and her colleague discuss integrating AI-native innovation with a human-centered design approach, focusing on how technology can be made accessible through natural interaction with AI and through rapid, user-friendly development flows. They begin by positioning AI as the new user interface. The other speaker notes that AI’s ease and approachability come from the ability to use human language, enabling conversations that let people interact with technology in a fundamentally new way. This language-based interaction is highlighted as a core shift in how users engage with digital tools and services. Beyond language, the conversation expands to include other modalities that users can employ to communicate with AI. The speakers identify text, images, and audio as essential inputs. The concept of multimodality is introduced to describe the ability to input using whatever format feels most natural to the user. Examples given include dropping in a screenshot, using voice to talk to the AI, or providing a video or a document. The emphasis is on a flexible, conversational experience that can accept diverse media and still deliver the necessary answers and help. The speakers then pivot to the question of how to create applications quickly and easily. They express enthusiastic interest in a partnership with Figma, a design platform. The collaboration is described as enabling designers who create an application design in Figma to hand off that design to a build agent, which can translate the design into an enterprise-grade application. This suggests a streamlined pipeline from design to production, leveraging AI to automate aspects of the development process and accelerate delivery while maintaining enterprise quality. Throughout, the emphasis remains on combining AI-driven capabilities with human-centered design principles to simplify interactions and speed up application development. The dialogue underscores the idea that users can engage with AI through natural language and multiple input formats, and that design-to-deployment workflows can be accelerated through integrated tools and partnerships. To learn more about AI experience, the conversation points listeners to a link in the comments, inviting further exploration of the described capabilities and partnerships.

Former Tesla AI director Andre Karpathy discusses software in the era of AI, emphasizing how software is changing at a fundamental level and what this means for students entering the industry. Key framework: three generations of software - Software 1.0: the code that programs computers. - Software 2.0: neural networks, where you tune data sets and run optimizers to create model parameters; the weights program the neural nets rather than hand-written code. - Software 3.0: prompts as programs that program large language models (LLMs); prompts are written in English, effectively a new programming language. - He notes that a growing amount of GitHub-like activity in software 2.0 blends English with code, and that the ecosystem around LLMs resembles a newer GitHub-like space (e.g., Hugging Face, Model Atlas). An example: tuning a LoRa on Flux’s image generator creates a “git commit” in this space. Evolving software stacks in practice - At Tesla Autopilot, the stack evolved from heavy C++ (software 1.0) to neural nets handling image processing and sensor fusion, with many 1.0 components being migrated to 2.0. The neural network grew in capability and size, and the 1.0 code was deleted as functionality migrated to 2.0. - We now have three distinct programming paradigms: 1.0 coding, 2.0 weights, and 3.0 prompts. Fluent capability in all three is valuable because tasks may be best solved with code, trained networks, or prompts. LLMs as a new computer and ecosystem view - Andrew Ng’s “AI is the new electricity” is cited to frame LLMs as utility-like (CapEx for training, OpEx for API serving, metered usage, low latency, high uptime) and also as fabs-like (large CapEx, rapid tech-tree growth), though software nature means more malleability. - LLMs are compared to operating systems: CPU-like core, memory in context windows, and orchestration of compute/memory for problem solving. App downloads can be run across various LLM platforms similarly to cross-OS apps. - The diffusion pattern of LLMs is inverted compared to many technologies: governments and corporations often lag behind consumer adoption, with AI topics sometimes used for everyday tasks like “boiling an egg” rather than high-level strategic aims. Practical implications for developers and students - Build fluently across paradigms: code in 1.0, tune 2.0 models, and design 3.0 prompts; decide when to code, train, or prompt depending on task. - Partially autonomous apps: exemplified by Cursor and Perplexity. - Cursor: traditional interface plus LLM integration, with under-the-hood embeddings, diffs, and multi-LLM orchestration; GUI support for auditing changes; autonomy slider lets users control how much the AI acts vs. what humans verify. - Perplexity: similar features, with sources cited and ability to scale autonomy from quick search to deep research. - Autonomy slider concept: users can limit or increase AI autonomy depending on task complexity; the AI handles context management and multi-call orchestration, while humans verify for correctness and security. - Education and “keeping AI on the leash”: emphasize concrete prompts, better verification, and development of structured education pipelines with auditable AI-generated content. Opportunities and caveats in AI-assisted workflows - Education and governance: separate roles for AI-generated courses and AI-assisted delivery to students, ensuring syllabus adherence and auditability. - Documentation and access for LLMs: docs should be machine-readable (e.g., markdown), and wording should be actionable (avoid “click” commands; provide equivalent API calls like curl) to facilitate LLM interactions. - Tools to ingest data for LLMs: services that convert GitHub repos into ingestible formats (e.g., git ingest, DeepWiki) to create ready-to-query knowledge bases. - Agents vs. augmentation: early emphasis on augmentation (Iron Man-like suits) rather than fully autonomous systems; the autonomy slider enables gradual handover from human supervision to more autonomous tasks while maintaining safety and auditability. - The future of “native” programming: vibe coding and byte coding illustrate how language-based programming lowers barriers, enabling broad participation in software creation; the takeaway is that natural-language interfaces can act as a gateway to software development, even for non-experts. Closing synthesis - We’re at an era where enormous code rewriting is needed, and LLMs function as utilities, fabs, and operating systems, though still early—like the 1960s of OS development. - The next decade will likely feature a spectrum of partially autonomous products with specialized GUIs and rapid verification loops, guided by an autonomy slider and careful human oversight. - Karpathy envisions an ongoing collaboration with AI: building partial autonomy products, evolving tooling, and experimenting with how the industry and education adapt to this new programming reality. He invites readers to participate in shaping this future.

"Open source AI models is a key building block for AI and basic research today." "A lot of AI models are accessible only behind a proprietary web interface where you can call someone else's proprietary model and get a response back, and that makes it a black box." "It's much harder for many teams to study or to use in certain ways." "In contrast, the team is releasing open models, open ways or open source models that anyone can download and customise and use to innovate and build new applications on top of or to do academic studies on top of." "So this is a really precious, really important component of how AI innovates."

Introducing our new course, generative AI for Everyone. Learn about the power of generative AI tools like ChatGPT, Googlebot, Microsoft ScreenChats, and MidJourney. Discover how generative AI works, its limitations, and how to effectively use it for work or leisure. This course is designed for non-technical individuals and doesn't require coding skills or prior AI knowledge. We'll focus more on text generation than image generation. Whether you're curious about generative AI, a professional exploring its impact on your work, or a business/government entity seeking new opportunities and risks, this course is for you. Sign up now and enjoy the course.

The speaker envisions a future where programming is largely mediated through natural communication with a computer. In this vision, you will tell the computer what you want in plain language, and the computer will respond with concrete outputs such as a build plan that includes all suppliers and a bill of materials aligned with a given forecast. The speaker emphasizes that the initial interaction is in plain English, and the computer can generate a comprehensive plan based on the stated requirements. If the output doesn’t meet the user’s preferences, the user can create a Python program to modify that build plan. A key example given is asking the computer to come up with a build plan with all the suppliers and the bill of materials for a forecast, and then relying on the computer to produce the necessary components in a cohesive plan. The speaker illustrates a workflow where the user can iterate by writing a Python program that adjusts the generated plan, thereby enabling customization and refinement of the suggestions produced by the initial natural-language prompt. The speaker then reiterates the concept of speaking with the computer in English as the first step, and implies that the second step involves using Python or programmable modifications to tailor the result. This underscores a shift in how programming is approached: the user first communicates in English to prompt the computer, and then leverages programming to fine-tune or alter the plan as needed. The underlying message is that the interaction with computers is evolving toward more intuitive human-computer dialogue, where the machine can interpret a plain-English prompt and produce structured, actionable outputs, with a programmable mechanism to adjust those outputs. Central to this discussion is the idea of prompt engineering—the practice of how you prompt the computer and how you interact with people and machines to achieve the desired outcome. The speaker highlights that prompting the computer and refining instructions is an art, describing prompt engineering as an artistry involved in making a computer do what you want it to do. The emphasis is on crafting prompts that elicit precise, useful results and on the skilled, creative process of fine-tuning instructions to achieve the best possible alignment between user intent and machine output.