March 15th, 2024: Apple has reportedly acquired DarwinAI, a Canada-based startup focused on artificial intelligence (AI), in a deal that could significantly boost the tech giant’s AI ambitions.

The acquisition, which took place earlier this year, has not been officially announced by Apple, but sources familiar with the matter have confirmed the development to Bloomberg’s Mark Gurman.

DarwinAI, known for its expertise in developing AI systems to inspect components in the manufacturing process visually, has also seen many of its employees join Apple’s AI division.

Most notably, the startup’s co-founder and Chief Scientist, Alexander Wong, has been hired as a Director of Machine Learning Research at Apple.

The acquisition comes when Apple CEO Tim Cook has emphasized the company’s focus on AI development.

During the company’s quarterly earnings call in February, Cook revealed that Apple was investing a “tremendous amount of time and effort” in AI and hinted at the possibility of fresh AI-driven features being unveiled later this year.

Speculation surrounding Apple’s AI plans has been rife, with rumors suggesting the development of a native foundation model called AppleGPT, an enhanced version of Siri that could function as a chatbot similar to ChatGPT and Google Gemini, AI-generated playlists for Apple Music, and AI integration into productivity apps like Pages and Keynote.

The acquisition of DarwinAI could play a crucial role in Apple’s AI strategy, particularly in developing on-device AI models and features.

This approach aligns with Apple’s emphasis on user privacy and security, as on-device AI processing minimizes the need for cloud-based data processing.

As Apple continues to invest in AI and expand its capabilities, integrating DarwinAI’s expertise and technology could lead to significant advancements in the company’s products and services.

While the specifics of the acquisition and its implications remain under wraps, the tech world eagerly awaits the curtain-raiser of Apple’s AI-driven innovations in the coming months.

March 17th, 2024: US-based startup Cognition introduced Devin, an AI-powered tool the company claims is the “world’s first fully autonomous AI software engineer.”

Devin is designed to solve engineering tasks independently using its own shell, code editor, and web browser.

According to demonstrations provided by Cognition, Devin can utilize its web browser to access and learn from API documentation, enabling it to plug into various APIs.

When the AI agent encounters an error, it automatically adds a debugging print statement to the main code within its code editor interface and reruns the code.

Cognition has showcased Devin’s capabilities in building and deploying apps, identifying and fixing bugs in codebases, and even fine-tuning AI models.

To assess Devin’s accuracy, Cognition tested the AI agent on SWE-bench, a benchmarking platform that challenges agents to resolve real-world issues found in open-source projects on GitHub.

Devin successfully resolved 13.86% of the issues end-to-end, surpassing the performance of GPT4 (1.74%) and the previous best score held by Anthropic’s Claude 2 (4.80%).

Notably, Devin achieved this without assistance in locating the relevant files within the repository.

While Microsoft offers AI-powered developer tools like GitHub Copilot, which provides code completion and assistive features for programmers, it cannot complete codes end-to-end without human interference or assistance.

In contrast, Devin is capable of autonomously completing coding tasks.

Today we’re excited to introduce Devin, the first AI software engineer.

Devin is the up-to-date state-of-the-art on the SWE-Bench coding benchmark, has successfully passed practical engineering interviews from leading AI companies, and has even completed real jobs on Upwork.

Devin is… pic.twitter.com/ladBicxEat

— Cognition (@cognition_labs) March 12, 2024

Cognition is currently offering early access to Devin for businesses who wish to utilize the AI agent for engineering work. Interested customers can request early access through the company’s website.

With its impressive performance on the SWE-bench platform and its ability to operate independently, Devin represents a significant step forward in the development of AI-powered software engineering solutions.

Course Overview

Welcome to GreatAIPrompt’s Complete Prompt Engineering Guide, your comprehensive guide to Generative AI and Prompt Engineering, designed for beginners and intermediates.

This introductory course is created for those curious about Generative AI, regardless of their technical background. Whether completely fresh to the concept or seeking deeper insights, this guide is your go-to resource.

What is Generative AI?

Generative AI, a subfield of artificial intelligence, is focused on creating outputs that are similar to human-generated content.

This includes a wide range of outputs such as text, images, graphics, music, and computer code.

The foundation of generative AI lies in its ability to learn the patterns and structures of the input data it’s trained on, allowing it to generate fresh data that shares similar characteristics.

Generative AI applications are immense and growing. They’re being used in customer support via AI-driven chatbots, in software development for code review and bug identification, and in content creation for writers.

In healthcare, generative AI is being explored for applications like accelerating drug discovery. It’s also used in digital marketing, education, finance, and environmental science.

Why Prompt Engineering?

Prompt Engineering¹ is an emerging discipline focused on crafting and refining prompts to effectively utilize Vast Language Models (LLMs)² like OpenAI’s GPT-3.5.

It’s about understanding the capabilities and limits of these models and using them to solve sophisticated tasks, ranging from uncomplicated question-answering to intricate problem-solving scenarios.

Prompt engineering is vital for leveraging the full potential of generative AI, enabling more capable and effective utilize of these tools across industries. However, it’s not a petty process. It requires balancing achieving desired results and ensuring safety, especially since AI models can sometimes produce unintended or harmful content³.

The energetic field requires a blend of linguistic skills and innovative thinking to fine-tune prompts for the best outcomes. This makes it an increasingly vital skill set in the era of AI-driven technologies.

Course Material and Methodology

All examples and exercises in this course are tested with GPT-3.5 and GPT-4, using OpenAI’s Playground and ChatGPT web app with default configurations. While this beginner course is centered around GPT-3.5, the principles apply broadly to other similar models.

How This Course Will Benefit You

• Non-technical and Accessible: Designed for simple comprehension, this module requires no technical background, making it ideal for beginners.
• Practical Examples and Exercises: Through real-world examples and interactive exercises, you’ll gain hands-on experience in using Gen AI and prompt engineering.
• From Basics to Advanced: Starting with the fundamentals, the course gradually takes you to more advanced concepts and applications.

A Glimpse into the Course

To illustrate the potential of prompt engineering, consider the task of writing essays.

With a well-crafted prompt, a Gen AI can compose funny yet academic essays, saving valuable time.

This course will guide you through crafting such prompts, helping you harness the full potential of Gen AI for various applications.

Join Us on This Learning Journey

As we launch on this exhilarating journey into the world of Generative AI and Prompt Engineering, we invite you to dive in with an open mind and a willingness to explore.

Just click the next page, and you’ll be taken to other modules of this course.

With each module, you’ll gain knowledge and practical skills that can be applied in numerous scenarios.

Let’s begin this transformative learning experience together!

March 18th, 2024: Renowned filmmaker and producer Tyler Perry has put his $800 million expansion plans for his Atlanta studio on indefinite hold after witnessing the capabilities of OpenAI’s fresh text-to-video model, Sora.

The AI technology, which debuted on February 15, allows users to create video images from text prompts, potentially revolutionizing the filmmaking process.

In an interview with The Hollywood Reporter, Perry expressed his astonishment at Sora’s capabilities, stating, “Being told that it can do all of these things is one thing, but actually seeing the capabilities, it was mind-blowing.”

The planned expansion would have added 12 more sound stages to Perry’s 330-acre production facility, one of the largest in the United States.

If I wanted to be in the snow in Colorado, it’s text,” he said. “If I wanted to write a scene on the moon, it’s text, and this AI can generate it like nothing.”

While acknowledging that Sora could enable filmmakers to produce movies and pilots at a fraction of the current cost, Perry expressed grave concerns about the potential impact on jobs in the entertainment industry.

“I am very, very concerned that in the near future, a lot of jobs are going to be lost,” he said. “It makes me worry so much about all of the people in the business. Because as I was looking at it, I immediately started thinking of everyone in the industry who would be affected by this, including actors and grip and electric and transportation and sound and editors, and looking at this, I’m thinking this will touch every corner of our industry.”

Perry admitted to using AI technology in two of his recent projects to avoid spending hours in aging makeup, but like other studios, he is still trying to navigate the implications of this rapidly evolving technology.

He emphasized the need for regulations to protect the industry and its workers, stating, “There’s got to be some sort of regulations in order to protect us. If not, I just don’t see how we survive.”

Looking beyond the entertainment industry, Perry called for a government approach to support everyone sustain employment in the age of AI.

“If you look at it across the world, how it’s changing so quickly, I’m hoping that there’s a whole government approach to help everyone be able to sustain,” he said.

As the entertainment industry grapples with the potential impact of AI technology on jobs and production processes, Tyler Perry’s decision to pause his studio expansion serves as a stark reminder of the challenges that lie ahead.

March 20, 2024: Nvidia, the US chipmaker with a market value of over $2 trillion, has extended its lead in the artificial intelligence sector with the announcement of several groundbreaking technologies at its annual developer conference on Monday.

The Blackwell B200 chip is a significant upgrade over Nvidia’s previous H100 AI chip, reducing the number of chips and power required to train a massive AI model like GPT-4 by 75%.

The company also introduced the GB200 “superchip,” which combines two B200 chips and Nvidia’s Grace CPU on a single board, offering “30x the performance” for server farms that run chatbots like Claude or ChatGPT, while reducing energy consumption by up to 25 times.

Nvidia CEO Jensen Huang showcased even larger configurations, such as the GB200 NVL72, a single server rack with 72 B200 chips connected by nearly two miles of cabling, and the DGX Superpod, which combines eight of these racks into a shipping-container-sized AI datacentre in a box.

In addition to the modern AI chips, Nvidia announced Project GR00T, a foundation model for controlling humanoid robots.

This model, paired with the Jetson Thor system-on-a-chip, aims to create autonomous machines that can be instructed using natural language to carry out general tasks, even those they haven’t been specifically trained for.

“Project GR00T will help robots understand natural language and emulate movements by observing human actions – quickly learning coordination, dexterity, and other skills in order to navigate, adapt, and interact with the real world,” Huang explained.

Nvidia also entered the quantum computing sector with the introduction of a cloud service that uses its AI chips to simulate quantum computers.

While the service will not initially be connected to a real quantum computer, it will allow researchers to test their ideas without the expense of accessing occasional and costly quantum hardware.

In the future, Nvidia plans to provide access to third-party quantum computers through the platform.

As Nvidia continues to push the boundaries of artificial intelligence and robotics, the company’s latest innovations showcase its commitment to leading the industry and shaping the future of these transformative technologies.

April 2nd, 2024: OpenAI, the company behind the popular ChatGPT, has announced Voice Engine, a up-to-date text-to-speech AI model that can create synthetic voices based on a 15-second segment of recorded audio.

The technology, developed in behind schedule 2022, has the potential to provide numerous benefits, such as reading assistance, global reach for creators, and personalized speech options for non-verbal individuals.

However, despite the potential advantages, OpenAI has decided to preview the technology but not widely release it at this time due to concerns about potential misuse.

The company initially planned to launch a pilot program for developers to sign up for the Voice Engine API earlier this month but scaled back its ambitions after considering the ethical implications.

In a statement, OpenAI said, “We are choosing to preview but not widely release this technology at this time. We hope this preview of Voice Engine both underscores its potential and also motivates the need to bolster societal resilience against the challenges brought by ever more convincing generative models.”

The company has been testing the technology with select partner companies since last year, requiring them to agree to terms of operate that prohibit impersonation without consent and mandate informed consent from individuals whose voices are being cloned.

OpenAI has also implemented a watermark in every voice sample to assist in tracing the origin of any voice generated by its Voice Engine model.

To address the potential risks associated with voice-cloning technology, OpenAI has provided three recommendations for society to adapt: phasing out voice-based authentication for bank accounts, educating the public about the possibility of deceitful AI content, and accelerating the development of techniques to track the origin of audio content.

The company emphasizes the need for a cautious and informed approach to the broader release of synthetic voice technology.

“We hope to start a dialogue on the responsible deployment of synthetic voices and how society can adapt to these new capabilities,” OpenAI stated. “Based on these conversations and the results of these small scale tests, we will make a more informed decision about whether and how to deploy this technology at scale.”

As the development of voice-cloning technology continues to advance, it is crucial for companies like OpenAI to consider the potential risks and ethical implications while working to harness the benefits for society.

July 14th, 2024: A recent video showing OpenAI CEO Sam Altman behind the wheel of a Koenigsegg Regera, a hypercar valued at over $1.9 million, has ignited a heated debate on social media.

The footage, whicth quickly went viral, raised questions about the financial practices of OpenAI, originally established as a non-profit organization.

The Koenigsegg Regera, a limited-edition sports car produced by Swedish automaker Koenigsegg, is known for its exclusivity and high price tag.

Only 85 models have been manufactured since its 2015 launch, with some units fetching up to $3.7 million at auction.

The video prompted a response from Tesla CEO Elon Musk, who co-founded OpenAI with Altman in 2015 but left the board in 2018 due to disagreements over the company’s direction.

Great question

— Elon Musk (@elonmusk) July 10, 2024

Musk’s terse comment, “Great question,” came in reply to a post questioning how OpenAI transitioned from a non-profit to a for-profit entity.

This incident has reignited discussions about the relationship between Musk and Altman, which has been strained in recent years.

In February 2024, Musk filed a lawsuit against OpenAI and Altman, alleging that they had abandoned the organization’s original goals.

The Koenigsegg Regera boasts impressive specifications, including a twin-turbo V8 combustion engine combined with three electric motors. The vehicle’s name means “to reign” or “to rule” in Swedish, reflecting its status as one of the world’s most prestigious hypercars.

As the video continues to circulate online, it has sparked a broader conversation about the financial structures of AI companies and the lifestyles of their executives.

The incident highlights the ongoing scrutiny faced by leaders in the tech industry and the convoluted relationships between key figures in the field of artificial intelligence.

 

Most organizations have AI work scattered across chat apps, notebooks, and scripts. That fragmentation slows iteration, hides useful work, and makes it difficult to standardize prompts or enforce guardrails. Weam, an open-source AI for teams fixes this by giving teams a shared layer—Brains—where conversations, prompts, agents, and documents live together with access controls. The result: reusable workflows, shared context, and less operational drag.

What is Weam

• Open-source AI for teams: Adopt, customize, and extend without black-box tools or lock-in.
• Multi‑LLM in one interface: Switch between models like GPT, Claude, and Gemini—even within the same conversation.

• Custom agents: Build task-specific assistants grounded in your organization’s knowledge and workflows.
• Connections via MCP: Connect Slack, GitHub, Google Workspace, and more so agents can act on the data you already have.
• Your deployment, your rules: Utilize the hosted cloud, one‑click cloud deployment, or self-host with Docker for full data control.

Brains: the hub for shared AI work

A Brain centralizes:

• Chats: Persistent, context-rich conversations that can be handed off across teammates.
• Prompts: Versioned, reusable templates that keep outputs on-brand and on-policy.
• Agents: Task-specific assistants with tools and domain knowledge baked in.
• Documents: Knowledge sources agents and chats can reference.
• Access controls: Organize Brains within Workspaces so the right people see the right assets.

How teams operate Weam across departments

• Sales
  • Utilize Pro Agents like the Sales Video or Audio Call Analyzer to summarize calls, extract objections, and auto-generate follow-ups.
  • Share insights in a Sales Brain so marketing and product can spot trends.
• Marketing
  • Store brand guidelines and briefs as Documents.
  • Utilize an SEO Article Generator agent to draft on-brand, search-optimized content for review in the same Brain.
• Support and QA
  • A QA Agent triages issues, generates test cases, and drafts responses from your knowledge base.
  • Keep answers current by linking GitHub or ticket/ticketing data via MCP.
• Product and Engineering
  • Centralize architecture docs and RFCs; build agents to answer technical questions, draft specs, or support with coding.
  • Pick the best model per task without juggling multiple tools or accounts.

Key benefits

• Single source of truth: Prompts, agents, and docs live together—no version hunting.
• Reusable workflows: Discover, copy, and adapt proven prompts and agents.
• Shared context: Limpid conversations and outcomes reduce duplication.
• Guardrails at scale: Standardize prompts and agents with permissions and controls.
• Model flexibility: Utilize the right LLM per step, without scattered subscriptions.

Integrate with your stack

• Slack for conversational workflows and quick handoffs.
• GitHub, Google Workspace, and more via MCP so agents can securely operate existing data.
• As it is an open-source AI for teams it keeps them in one interface.

Control, security, and deployment options

• Cloud: Start instantly with the hosted version.
• One‑click cloud deployment: Launch on AWS or DigitalOcean with automated scripts.
• Self‑host: Run on your own infrastructure with Docker for full data control and compliance.
• Local development: Ideal for contributors and teams prototyping agents and prompts.

Get started in minutes

1. Create a Workspace and add teammates.
2. Spin up a Brain for a department or project.
3. Upload key documents and connect Slack/GitHub/Google via MCP.
4. Save your best prompts as shared templates.
5. Build a custom agent for a high‑leverage task (e.g., lead qualification, content drafts, QA triage).
6. Iterate together—chats, prompts, and agents evolve in one place.

Open-source and community-driven

Weam is open-source and welcomes contributions across code, docs, and discussion. Adopt it as-is, customize it, or extend it with recent agents and integrations—on your timeline, not a vendor’s.

Why teams choose Weam

• Centralized collaboration with Brains
• Multi‑LLM access in one interface
• Custom agents grounded in your data
• Integrations through MCP
• Self‑host for complete control

Try Weam and explore the docs

• Weam: Open-Source AI for Teams
• Weam AI Cloud
• Documentation
• Choose your setup: cloud, one‑click deployment, or self‑host with Docker

With Brains as the shared layer for chats, prompts, agents, and knowledge, Weam turns scattered AI experiments into scalable, cross‑functional workflows your whole organization can trust.

Adopt AI Your Way to Build Better

Weam is an open-source for teams AI. A platform that brings chats, prompts, agents, and documents into shared spaces called Brains. It lets teams operate multiple LLMs in one place, integrate tools via the Model Context Protocol (MCP), and deploy in the cloud or self-host—so you get faster collaboration, better knowledge reuse, and no vendor lock-in.

Frequently Asked Questions

Many of us find that using to-do list apps is a astute way to plan our day. These apps let us write down our tasks and decide which ones are most critical. This helps us get more done.

But what if we told you that these to-do list tools could be even better with the assist of AI?

Yes, artificial intelligence has made these apps more useful. It can assist with tasks like sorting your to-dos, setting reminders, or suggesting next steps.

So, we’ve searched for you and have curated a list of the top AI to-do list tools to make you even more productive.

Let’s dive in!

We get it; the team has we all have used established to-do list tools for years.

They are uncomplicated and straightforward, and they get the job done. Put down a task, tick it off when it’s done—what’s not to love?

Data shows that 89% of people who use any to-do list feel more in control of their day. We’ve felt that too.

But after trying AI-powered to-do list tools, it felt like we moved from riding a bicycle to driving a Buggati. The best AI tools can prioritize tasks for you, suggest the best times to work on them, and even send you reminders based on your past behavior.

Some of us were initially skeptical, but the benefits quickly became apparent.

So, should you ditch your senior to-do list app? Not necessarily. Established to-do lists are still great for uncomplicated tasks and offer a level of control some people prefer.

But if you’re looking to up your productivity game, AI-enhanced to-do list tools are definitely worth a try.

We found that they work best when managing multiple tasks that vary in importance and due date. They assist you focus on what matters, saving you time and stress.

In low, we’re not saying AI to-do list tools are for everyone, but they have made a world of difference for us. Based on the data and our experience, the switch is worth considering.

So, if you’re not skeptical and want to try the fresh tech, check out the AI to-do list tools below.

We’ve tried many to-do list apps, and after lots of testing, reading reviews, and even more cups of coffee, we’ve finally narrowed it down to these exceptional to-do list AI tools that you should consider.

1. Taskade

For those of you who are tired of switching between a dozen apps, Taskade could be your one-stop solution.

You can make lists, write notes, and chat with your team in the same app.

Users love the slash commands, which bring up a variety of AI suggestions.

From summarizing meeting notes to creating project templates, it helps in many ways. You can apply it for free, but there’s also a pro version with even more features.

Features:

Pricing:

• Free: 1,000 AI credits/month
• Starter: $4/ month for up to 3 users
• Plus: $8/ month for 6 users

The Good and The Bad

2. Todoist

Todoist is a widely used task management program that has facilitated the productivity of millions. Adding an AI assistant will escalate your ‘to-do list’ efficiency by 10x.

By generating a list of activities, making tasks more actionable, breaking down frightening jobs, and offering recommendations on achieving tasks, Todoist, one of the world’s most popular task managers, may make goals more accessible.

You might benefit from an AI assistant if you have trouble remembering things or setting priorities.

Features:

Pricing:

• Free: 5 vigorous projects
• Pro: $5/ month billed monthly Or $4/ month billed yearly

The Good and The Bad

3. Trevor AI

While finding the best AI to-do list tools, we found that people like Trevor AI because it takes the guessing out of when to do things.

It uses this thing called “time blocking” to put your tasks in the best spots in your calendar. Trevor even learns how you work, so it gets better over time.

Users say it’s like having a astute personal assistant that helps plan your day. And it’s web-based, so you can access it from any device.

Features:

Pricing:

Pro Plan: $3,99/month Or $29,99/year.

The Good and The Bad

4. Sidenote AI

This one’s a Chrome extension that’s effortless to install and apply. Users tell us it’s like having a little helper while you’re browsing or in a meeting.

Sidenote AI can turn your notes into emails and even set reminders. It works well with other apps like Notion and Google Docs, making it a versatile choice for many.

Did we mention that it’s also backed by YC (Y Combinator)?

Features:

Pricing:

Free: You can apply it for free as it is a Chrome extension.

5. Notion AI

Notion is known for being a great note-taking app, but users love the AI that helps with to-dos too. It scans your notes and can convert them into tasks. It even corrects your spelling as it goes, which is a nice bonus.

People say it’s like having an extra pair of eyes that’s always on the lookout to assist you be more organized.

Features:

Pricing:

• Free: Free
• Plus: $8 per user/month billed annually, $10 billed monthly
• Business: $15 per user/month billed annually, $18 billed monthly
• Enterprise: Request a demo or Request a Trial

The Good and The Bad

6. TimeHero

According to reviews, TimeHero is all about automating your schedule. No need to figure out what to do when; TimeHero does it for you.

Users mention that they really appreciate how it considers what you’re already busy with so you don’t get double-booked.

It offers a 7-day free trial, which people say is enough time to see if it’s right for you.

Features:

Pricing:

• Basic: $4.60 per user, per month
• Professional: $10 per user, per month
• Premium: $22

The Good and The Bad

7. BeforeSunset AI

This tool looks at your calendar and tells you what you can get done before, you guessed it, sunset!

Reviews say the time-tracking feature helps you become more aware of how you’re spending your time.

It also has tags to assist you categorize your tasks, which users find really neat.

Features:

Pricing:

• Free: AI assistant (300 credits)
• Pro: $12.99/per month Or annually, $9.99/per month. AI assistant (Unlimited)

8. Taskheat

Do you know how sometimes tasks depend on other tasks? Like, you can’t make a sandwich until you’ve bought bread?

Taskheat shows this with a flowchart. We heard from users who love this because it helps them see the massive picture.

The downside is that it’s only for Apple users, but those who apply it rave about how intuitive it is.

Features

Pricing:

One-time: $14.99. Taskheat has a 14-day free trial, so you have two weeks to see if it fits your style.

The Good and The Bad

FAQs

The End

We’ve gone through many tools, and each has its features. Whether you’re planning your day, your team’s day, or just trying to remember to buy bread, there’s probably a tool here for you.

We’ve heard good things about all these tools, so maybe try one or two. Don’t forget to mention your favorite in the comment section.

Let’s assist the AI community together.

Cheerful organizing!