Greetings, fellow members of this brilliant community! I am thrilled to unveil a remarkable creation that will revolutionize your project management experience. Behold, WiseTimer Pro - the ultimate tool to elevate your focus, optimize your time, and help you achieve unprecedented levels of productivity.
Prepare to be astounded as I present to you this magnificent Coda template, designed to simplify and streamline your project management endeavors. With a mere word and the click of a button, you can effortlessly generate a comprehensive array of project tasks and subtasks, complete with estimated due dates for each subtask, and much more.
How it Works?
Submit a project’s name, and prepare to witness the magic unfold.
Once armed with the project description, we transcend conventional boundaries, swiftly crafting an intelligently curated list of tasks that align seamlessly with your project’s name and description.
But here’s where the true genius emerges. We utilize Coda automations to loop effortlessly over the task list and add entries within the tasks table.
Each task deserves its own essence, and Coda AI transforms mere task names into captivating descriptions, breathing life into every step of your project.
Drawing inspiration from these remarkable task descriptions, we can craft a list of subtasks that resonate perfectly with each individual task, ensuring every aspect of your project receives the attention it deserves.
With this meticulously crafted list of subtasks at hand, we work our magic once more, seamlessly transforming them into tangible entries within the subtasks table. Every detail is captured effortlessly, leaving you free to channel your energy towards conquering the challenges that lie ahead.
Enter the captivating world of the workplace section on the home page, where each individual task beckons you to unleash your expertise. Immerse yourself in the realm of creativity and productivity, effortlessly tackling each task with confidence and finesse.
Prepare to embark on a journey where your projects flourish with ease, courtesy of this ingenious automation. Unleash your genius potential and let WiseTimer Pro guide you towards unparalleled project management success.
Conclusion
So, my esteemed colleagues, waste no time and embark on this remarkable journey with WiseTimer Pro. Elevate your project management prowess to unprecedented heights, and unlock a world of endless possibilities. Together, let us conquer the realm of productivity and innovation!
I was excited to try this template. Splitting tasks into subtasks is perhaps one of the biggest pain points for me.
I tried solving that with Coda AI myself but what it generated was pretty much gibberish. The subtasks were some generic made-up stuff that wasn’t relevant to what I was doing. Maybe if I provided more context to GPT, it would come up with a better set of subtasks. But at that point it would just be easier to type out the subtasks myself.
I honestly hoped this template did some magic to actually produce better subtasks. But no, unfortunately, it’s still the same irrelevant lot
I guess this is just not what AI can reliably solve yet. Understandably, it can’t read our minds.
Two things stand in the way of successfully generating direct tasks or sub tasks.
Embeddings. You need these to guide the LLM to perform iterative tests to force the LLM to generate new inferences until it reaches a quality threshold.
Langchain (or a mimicking of Langchain) in Coda AI. It is indeed possible, but it is a lot of work to build as a reusable template.
This is a use case where each additional layer of output granularity requires that your prompts generate results that themselves must be used in subsequent prompts. This is completely doable. It’s just not practical unless it’s for a client.
I appreciate your critique, and I agree that sometimes subtask names can be less than helpful. There are instances where a subtask is simply named “subtask” without much description. However, if your project has a more descriptive name like “Fitness Web App Development,” you can guarantee that both the tasks and subtasks will have meaningful names and descriptions. Similarly, when it comes to task names like “Implement progress tracking feature,” they should be clear and indicative of what needs to be done to guide the LLM.
It’s worth noting that earlier versions of this template were not very practical until about five days ago. Since then, improvements have been made to make it more useful, although it’s still not perfect.
You can mimic LangChain in Coda AI. The concept is relatively simple; the execution is a bit complex.
Create a table where fields will represent cascading prompts.
The first AI field includes a prompt that generates tasks about a specific topic.
The output of the first AI field (i.e., a list) needs to be iterated across by an intermediate field that uses a formula to parse the list and generate an array of new prompts.
Then, the next AI field must contain the combined output from each of the prompts in the array.
This is a simplified example. Imagine a more complex objective where there are many layers of AI automation that fire sequentially to satisfy a defined objective. Also imagine a level of intelligence that would allow the process to validate output and if not satisfactory, continue to improve the generated prompts and test until the quality of output is satisfactory and grounded in reality.
I am working on contract logic and try to get the AI output in line with my instructions based on the input (client details, choices, contract type, purchase, conditions etc) . Such an approach may work, I’ll keep testing it. As you mention, it is time consuming and writing prompts is not easy (not even with your suggestions).
It also presupposes a good understanding of how to Coda.
Yes! Promptology was built with almost every significant Coda feature. The AI part is only 20% of the system. It’s an important piece, of course, but it’s small compared to the overall system design.
It’s been my experience that really good AI solutions are less about AI and much more about everyday data and content management workflows. If you’re good with building Coda solutions, you’ll be great with Coda AI.
I agree very much, but this may be an inconvenient truth for those who hope to skip the learning part regarding Coda (the Coda Language, the data architecture, the action logic (buttons, automations and so on).
To use AI to summarize something, to evaluate feedback, to prepare an email is nice, but that you can do without Coda. I know people who evaluate people according certain criteria, they paste the results in Chat GTP and with some instructions they get their final review in well written French… They don’t want to learn Coda for this, they are happy with what they have.
Making the AI work for you, blending structured and unstructured data, that is the real trick in which AI can be of great value. We keep building, testing and evaluating. It is still early!
The latest usage data from OpenAI says they aren’t all that happy. And this statement from OpenAI’s CEO identifies a huge issue for ChatGPT -
ChatGPT plugins aren’t really taking off because people want ChatGPT in their applications, not applications in ChatGPT . They wanted the UX more than the model.
But I get it; AI is not nearly baked into solutions the way they should be and almost none of what we do with AI presently uses any data in learner shots.
Sorry for the delayed response. Good points raised here! However, let’s not make things overly complex. We can easily simulate something simple like that using Coda AI and automations.
This is a use case where each additional layer of output granularity requires that your prompts generate results that themselves must be used in subsequent prompts. This is completely doable. It’s just not practical unless it’s for a client.
Yup!
Verbose confabulation and tasks necessary to complete a project, don’t mix. Ergo, lots of editing lay ahead.
As highlighted in the video, the primary objective is to bring your project to life. By breaking it down into tasks and subtasks, you can gain insights and inspiration regarding how your project might shape up.
Simulating embeddings seems difficult. Bear in mind, an embedding takes 1/600th the cost of a text completion, and runs in milliseconds, not multiple seconds as do chat and text completions. I can imagine vectorizing 2500 rows, but not with 2500 chat completions.
