He stated he’s pursuing this project because semiconductor manufacturers are not producing chips fast enough to meet his companies’ AI and robotics demands. Musk said:

“We either build the Terafab or we don’t have the chips, and we need the chips, so we build the Terafab.”

According to Bloomberg Musk shared his plans during an event in downtown Austin, Texas, with a photo indicating that the “Terafab” facility will be “gigafactory,” located near Tesla’s Austin headquarters.

He also added that the aim is to produce chips capable of supporting 100–200 gigawatts of computing power annually on Earth, as well as one terawatt in space. He did not provide a timeline for the plan.

Image: Presentation of the Terafab project

In the conversation with venture capitalist Bill Gurley, Tan said.

I sleep, like, four hours a night right now,

He added jokingly

I have cyber psychosis, but I think a third of the CEOs that I know have it as well,

Tan compared his work with AI to rebuilding a startup that previously required significant time, funding, and even stimulant use.

Once you try it, you’ll realize: It’s like I was able to re-create my startup that took $10 million in VC capital and 10 people, and I worked on that for two years, and I took anti-narcoleptics — I remember, you know, sort of being on modafinil,

He claims now AI has replaced the need for such aids.

I don’t need modafinil with this revolution. Like, I’m up. I slept at 4 a.m. I woke up at 8 a.m., I wanted to sleep more, but I couldn’t because: Let’s see what’s going on with the 10 workers. I’ve got like three different projects going right now.

Shortly before the interview, Tan released his Claude Code setup, called “gstack,” as an open-source project on GitHub. The system includes a collection of reusable “skills” — reusable prompts stored in “skill.md” files that guide AI behavior across roles such as CEO, engineer, and code reviewer.

Currently the gstack GitHub repository lists 13 skills, but Tan continues to tweet about new updates.

I’ve been having such an amazing time with Claude Code, I wanted you to be able to have my exact skill setup,

he wrote on X.

The project quickly gained traction, attracting nearly 20,000 GitHub stars and thousands of “forks”, while also trending on Product Hunt. However, it also sparked criticism after Tan claimed a CTO friend described it as “god mode” for identifying a security flaw.

Some developers dismissed the project as overly hyped. Critics argued it amounted to little more than a set of prompts, noting that many engineers already use similar workflows.

The youtube video “AI is making CEOs delusional” by Vlogger Mo Bitar is one example of the many critics.

Despite the backlash, AI systems themselves responded positively when asked to evaluate gstack. ChatGPT described it as “reasonably sophisticated prompt workflows” and highlighted the value of simulating an engineering team structure. Gemini called it a “Pro” configuration that improves correctness, while Claude praised it as “a mature, opinionated system built by someone who actually uses it heavily.”

In a follow-up post, Tan reiterated his enthusiasm for AI coding, writing,

I took modafinil just to stay awake longer to be able to turn the momentary crystalline structures I had in my brain into lines of code before sleep or human distraction turned it to grains of sand. I love coding but I love coding with AI even more. I speak it listens and we create. I see the structure and it is built. There is no more powerful an experience to me than that.

Image: Garry Tan LinkedIn Profile

The legal action comes after Defense Department officials designated Anthropic a supply chain risk, citing national security concerns. The move followed a statement by the company’s CEO, Dario Amodei, who said Antropic would not permit Claude’s AI model to be used for autonomous weapons, or for surveillance of U.S. citizens.

According to the lawsuit, the administration’s decision effectively places the AI company on a blacklist that blocks Pentagon suppliers from using Claude. That’s an attempt to punish the company over its AI guardrails.

Anthropic at a Crossroads: Pentagon Tensions, $380 Billion Valuation and the Future of AI

The federal government retaliated against a leading frontier AI developer for adhering to its protected viewpoint on a subject of great public significance — AI safety and the limitations of its own AI model — in violation of the Constitution and laws of the United States,

according to Anthropic, also adding that Trump officials “are seeking to destroy the economic value created by one of the world’s fastest-growing private companies.”

The supply-chain risk designation came after a meeting in February between Defense Secretary Peter Hegseth and Anthropic’ CEO Dario Amodei. According to national security experts, such a label is usually reserved for foreign adversary contractors that could pose a threat to U.S. interests, which makes the use of the blacklist against an American company highly unusual.

Following the designation, Donald Trump made a social media post stating that all federal agencies would stop using Anthropic’s AI tools.

While Anthropic was the first AI frontier lab used by U.S. officials on classified networks since the feud began, Pentagon officials have said Elon Musk’s xAI and OpenAI’s ChatGPT have now been cleared for use in classified systems.

Despite Anthropic’s strong resistance against the administration on lethal weaponry and mass surveillance, the company notes in it’s lawsuit that since 2024 it has collaborated with national security contractors, such as Palantir, to support the government in operations. Some of these activities include “rapid processing of complex data, identifying trends, streamlining document review, and helping government officials make more informed decisions in time sensitive situations.”

Image: Flickr/World Economic Forum/ Sandra Blaser; Edited – 10.03.2026

Image: U.S. Department of Defense / Chad J. McNeeley (Public Domain), via Wikimedia Commons. – 10.03.2026

AI First, But Not Chatbot First

While many tech companies rushed to integrate chatbots into their apps, Airbnb took a different route.

We started by solving the hardest problem, customer support,

Chesky said during the call.

The company built a custom AI agent trained on millions of Airbnb’s historical support interactions. According to Chesky, the system is already resolving one-third of customer support cases without human intervention — significantly reducing resolution times. The AI support agent is currently live across North America, with a global rollout planned.

Toward an AI-Native Airbnb App

Customer support is only the beginning. Chesky described a broader vision of an “AI-native” Airbnb experience.

We’re building an AI-native experience where the app doesn’t just search for you, it knows you,

he said.

In practical terms, that means AI will eventually help guests plan entire trips — not just book accommodations. It will also assist hosts in running their businesses more effectively and help Airbnb operate more efficiently at scale.

To lead this transformation, Airbnb hired Ahmad Al-Dahle as CTO. Al-Dahle previously spent 16 years at Apple and most recently led the generative AI team at Meta that built the Llama model. Chesky called him “one of the world’s leading AI experts,” highlighting his ability to combine large-scale technical systems with design-focused user experiences.

AI as a Competitive Moat

Airbnb’s AI ambitions also serve as a defensive strategy in a world increasingly shaped by AI search and aggregators.

This approach is also our strongest defense against disintermediation,

Chesky said.

While chatbots might surface listings from across the web, Airbnb argues that its proprietary ecosystem creates a barrier to replication.

A chatbot can give you a list of homes, but it can’t give you the unique points you find on Airbnb,

Chesky said.

He pointed to Airbnb’s 200 million verified identities, 500 million proprietary reviews, integrated messaging between guests and hosts (used by 90% of guests), global payment infrastructure, customer support, and insurance.

By layering AI over the entire Airbnb experience, we believe we’re building something that’s impossible to replicate,

he added.

AI Inside the Company

The AI push extends internally. Airbnb said that 80% of its engineers are already using AI tools in their workflows — with a goal of reaching 100%.

Spotify Co-CEO: “Our Best Developers Have Not Written a Single Line of Code Since December”

That level of adoption signals that AI is not just a product initiative but a productivity multiplier across the organization, potentially accelerating feature development, services expansion, and experimentation.

Beyond Homes: Services, Hotels, and the ‘Airbnb Trip’

AI will also play a role in Airbnb’s broader product strategy, which Chesky described as the “Airbnb trip.” The company has been expanding beyond accommodations into services and experiences, launching them globally in May but scaling city by city.

We are one app and one brand, where every part of the trip makes the other parts stronger,

Chesky said.

Airbnb has started in cities like Paris for experiences and Los Angeles for services, while also testing new offerings such as grocery delivery and airport pickups. The company is also bringing boutique and independent hotels onto the platform, calling the opportunity “massive.”

AI will serve as connective tissue across these verticals — helping users move fluidly from booking a home to reserving an experience, ordering services, or selecting a hotel — all within a personalized interface.

The Long Game

Airbnb emphasized that it is still early in its AI journey but signaled strong expectations around product and services growth, platform enhancements, and the impact of AI investments.

Unlike companies that treat AI as a bolt-on feature, Airbnb is attempting to re-architect its entire platform around intelligence, identity, and integrated services.

Material by Iva Abadjievа

Image: Wikipedia, Portrait of Brian Chesky, 2025

When applied with discipline and oversight, ChatGPT becomes a shared intelligence layer across the software lifecycle.

Benefits for Software Developers

Accelerated Development and Prototyping

ChatGPT significantly reduces the time required to generate boilerplate code, scaffolding, and common patterns.

Example:
A backend engineer uses ChatGPT to generate a REST API endpoint with authentication and validation logic. The generated code serves as a solid baseline, allowing the developer to focus on domain-specific logic and security hardening.

Value:

• Faster MVP and feature delivery
• Reduced repetitive work
• More time for architecture and problem-solving

Faster Learning and Context Switching

Modern developers frequently work across multiple languages, frameworks, and platforms. ChatGPT acts as an always-available technical reference.

Example:
A developer moving from Java to Go requests examples of concurrency patterns and common pitfalls.

Value:

• Shorter learning curves
• Fewer interruptions for documentation searches
• Higher confidence when adopting new technologies

Debugging and Code Quality Support

ChatGPT can analyze error messages, logs, and code snippets to suggest fixes and refactoring ideas.

Example:
A developer pastes a stack trace and receives likely root causes and remediation steps.

Value:

• Faster issue resolution
• Cleaner, more maintainable code
• Knowledge sharing embedded in daily work

Benefits for QA Engineers

Smarter Test Design

ChatGPT can generate structured test cases directly from requirements, user stories, or API definitions.

Example:
A QA engineer requests positive, negative, and edge-case scenarios for a payment workflow.

Value:

• Improved test coverage
• Reduced manual effort
• More consistent test documentation

Test Automation Enablement

For teams scaling automation, ChatGPT helps generate and explain test scripts in frameworks such as Selenium, Cypress, or Playwright.

Example:
Generating an end-to-end Playwright test for a checkout process, including assertions and retries.

Value:

• Faster automation adoption
• Lower barrier for non-developer QAs
• More maintainable automated tests

Exploratory Testing Enhancement

ChatGPT can suggest non-obvious user behaviors and failure scenarios.

Example:
Identifying edge cases for mobile network interruptions or unusual user flows.

Value:

• Earlier defect detection
• More resilient products
• Improved user experience

Benefits for DevOps Engineers

Infrastructure and Configuration Support

ChatGPT assists with Infrastructure as Code, Kubernetes manifests, and cloud configuration explanations.

Example:
Generating a Terraform module for a scalable cloud service with best-practice defaults.

Value:

• Faster environment provisioning
• Fewer configuration errors
• Easier onboarding for new team members

CI/CD Pipeline Optimization

ChatGPT can analyze pipeline failures and propose improvements.

Example:
Reviewing a failing CI pipeline and suggesting caching, parallelization, or dependency fixes.

Value:

• More reliable deployments
• Shorter feedback cycles
• Reduced operational friction

Incident Response and Documentation

ChatGPT helps teams summarize logs, draft postmortems, and maintain runbooks.

Example:
Creating a first draft of an incident report based on logs and timeline inputs.

Value:

• Faster incident recovery
• Better operational transparency
• Improved cross-team communication

Best Practices for Enterprise Use

• Treat ChatGPT as a copilot, not an autopilot
• Always review and validate AI-generated output
• Combine AI usage with code reviews, testing, and security checks
• Standardize prompts and usage patterns across teams

The Takeaway for Engineering Teams

For professional software teams, ChatGPT is becoming a strategic productivity tool across development, QA, and DevOps. By reducing cognitive load and accelerating routine tasks, it allows engineers to focus on higher-value work—design, reliability, and innovation.

Organizations that integrate ChatGPT thoughtfully into their engineering workflows gain not only speed, but also stronger collaboration, higher quality, and greater operational maturity.

Material by Iva Abadjievа

Image: AI Generated

In recent remarks, Huang said the company’s growth in AI is no longer driven solely by chip design, but by the ability to deliver end-to-end AI platforms that combine hardware, software, networking, and large-scale systems. That strategy is shaping where NVIDIA is hiring—and which skills it values most.

Key AI roles NVIDIA is prioritising

Huang indicated that NVIDIA’s hiring focus spans several high-impact technical areas:

• AI and machine learning engineers working on model optimisation, inference efficiency, and deployment at scale
• Software engineers specialising in CUDA, AI frameworks, compilers, and developer platforms
• Data-centre and systems engineers integrating GPUs, networking, power, and cooling for large AI clusters
• Cloud and AI infrastructure specialists supporting hyperscalers, enterprises, and sovereign AI initiatives
• Research scientists advancing next-generation AI architectures, performance techniques, and training methods

The emphasis reflects NVIDIA’s belief that its competitive edge lies in deep integration across the AI stack, rather than in hardware alone.

Why talent matters more than ever

Huang has stressed that as customers explore alternative accelerators and custom chips, NVIDIA’s software ecosystem and engineering expertise remain difficult to replicate. He has described people as one of the company’s most durable advantages, particularly in areas such as high-performance computing, distributed systems, and energy-efficient AI workloads.

Despite broader volatility in the technology job market, NVIDIA continues to signal that AI-focused hiring remains a priority, even as some peers slow recruitment or restructure teams.

What this means for AI professionals

For engineers and researchers, NVIDIA’s hiring priorities point to where long-term demand is strongest. Skills in infrastructure, optimisation, and production-grade AI systems are increasingly valued over narrow or experimental roles.

As AI shifts from research to critical enterprise and national infrastructure, NVIDIA’s message is clear: the next phase of AI growth will be built by specialised teams, not just faster chips.

Material by Iva Abadjievа

IMAGE: NVIDIA

But like any emerging practice, vibe coding has its sweet spots—and its traps. Here’s a clear-eyed guide to the dos and don’ts of vibe coding, for developers who want speed and substance.

What Is Vibe Coding?

Vibe coding is a style of development driven by momentum and intuition. Instead of rigid upfront planning, developers rely on rapid feedback loops, conversational AI tools, and a strong sense of direction to “feel” their way through a solution.

It thrives in:

• Prototyping and MVPs
• Hackathons and side projects
• Early-stage product exploration
• Creative problem-solving sessions

But vibe coding isn’t an excuse for chaos. The difference between magic and mess comes down to how you practice it.

The Dos of Vibe Coding

Do Use Vibe Coding for Exploration

Vibe coding shines when the problem space is fuzzy. Let yourself experiment, generate ideas quickly, and test assumptions without overthinking architecture too early.

Think:

What if we try this?

instead of

What’s the perfect solution?

Do Lean on AI, But Stay in Control

AI copilots are core to modern vibe coding. They help you scaffold, refactor, and brainstorm at speed. The key is intentional prompting and active review.

You’re still the architect. AI is your amplifier, not your replacement.

Do Protect the Flow State

Minimize friction:

• Reduce context switching
• Avoid premature optimization
• Keep feedback loops tight

When you’re in the zone, velocity compounds. Vibe coding works best when interruption is the exception, not the norm.

Do Refactor After the Vibe

Messy first drafts are fine—as long as you clean them up. Schedule time to:

• Rename variables
• Improve structure
• Add comments and tests

Vibe coding is phase one. Professional coding is phase two.

Do Communicate the “Why”

If you’re working on a team, document intent. Vibe-driven decisions can look arbitrary to others unless you explain the reasoning behind them.

A short README or commit message can save hours of confusion later.

The Don’ts of Vibe Coding

Don’t Skip Fundamentals

Vibe coding doesn’t override:

• Security best practices
• Performance considerations
• Data integrity
• Accessibility

If you don’t understand what the code is doing, you’re not vibe coding—you’re gambling.

Don’t Ship Vibes to Production

Production systems need reliability, observability, and maintainability. Pure vibe code without review, testing, and structure becomes technical debt—fast.

Rule of thumb:
If users depend on it, it deserves rigor.

Don’t Confuse Speed with Progress

Writing a lot of code quickly feels productive—but velocity without direction is just motion.

Pause occasionally and ask:

• Are we solving the right problem?
• Is this still aligned with the goal?

Don’t Ignore Your Future Self

Vibe coding today shouldn’t punish you tomorrow. If the code will live longer than a weekend, invest at least minimal effort in clarity.

Future-you is a teammate. Treat them well.

Don’t Assume Vibe Coding Is for Everyone

Some developers thrive in structured environments. Others flourish in creative flow. High-performing teams respect both styles and apply them deliberately.

Vibe coding is a tool—not a mandate.

Vibe first. Validate fast. Engineer responsibly.

Material by Iva Abadjievа

Image: AI generated, Deep Infra

Artificial intelligence is often discussed in abstract terms—models, scale, efficiency. But inside Amazon, some of the company’s most senior leaders are experiencing AI in far more personal, human ways: helping a dog gear up for kayaking, turning family recipes into conversations, organizing chaotic family schedules, and even discovering better books.

Across teams—from retail and sustainability to transportation, advertising, and devices—Amazon executives describe AI not as a distant technology, but as a daily companion that adapts, remembers, and increasingly feels intuitive.

Shopping That Knows You (and Your Dog)

For Doug Herrington, CEO of Worldwide Amazon Stores, AI’s impact is clearest in how it personalizes everyday shopping.

I’ve been using Rufus, Amazon’s AI-powered shopping assistant, to shop recently. I talk to Rufus about Arno a lot,

Herrington says, referring to his dog.

Rufus remembers his breed, his size, and his favorite food and treats.

That memory paid off when Herrington mentioned a kayaking trip in Puget Sound.

When I told Rufus about our upcoming outing, it recommended a great life vest for him without hesitation.

Beyond recommendations, Rufus is also changing how customers interact with pricing.

You can go to any product detail page and press the ‘price history’ link… and ask Rufus to alert you if the price drops, and even automatically buy it for you once it does,

Herrington explains.

I’ve got price alerts for Arno’s fetch toys and chew rings—so if they go on deal, Rufus loads us up. I’m happy—and Arno is too.

AI as a Connector, Not a Replacement

For Kara Hurst, Chief Sustainability Officer, AI has become a bridge—between family members and between complex ideas.

My parents live across the country, but music helps us stay connected,

Hurst says.

My son and I used AI to surprise my dad with custom songs based on his interests.

The result exceeded expectations.

The app produced country and rock tracks—good ones!—and my dad was absolutely blown away. It’s a great family memory.

AI also plays a practical role in her nonprofit work.

Outside of work, I serve on the board of Water.org, and AI has become invaluable for meeting prep,

she says.

I recently used it to summarize research and pull salient points from a long document, reminding me of the key questions I wanted to ask.

Better Books, Fewer Misses

For Beryl Tomay, Vice President of Transportation, AI has quietly reshaped how she reads.

Reading is a big part of my day,

Tomay says.

To help me choose what to read next, I added all my past books, ratings, and notes into an AI tool.

The system didn’t just learn what she liked—it learned what she didn’t.

The AI identified patterns and extrapolated things I tend to not enjoy, so the recommendations have been very aligned with what I like across a diverse set of genres.

The impact is measurable.

My yearly average book rating has even gone up as a result,

she notes.

Some of my favorite books from 2025 were found this way—and 2026 is already off to a strong start with two 5-star reads.

An AI “Chief of Staff” for Family Life

For Kelly MacLean, Vice President at Amazon Ads, AI helps manage something far more complex than campaigns: a household with three kids, two careers, and a dog.

The family calendar can feel like its own full-time job,

MacLean says.

I started experimenting with AI as a lightweight ‘AI family operating system’—something that thinks through logistics like a human chief of staff.

By connecting calendars, school schedules, sports, and travel, the system creates clarity.

Every Sunday it summarizes the week, flags conflicts before I ever see them, and offers daily adjustments that help us avoid scrambling.

It even handles the small things.

Snack duty, jersey colors, when to leave based on traffic, weather—it offloads the mental juggle,

she says.

Honestly, it almost feels a little magical.

Coding, Cooking, and Reading—Together

For Panos Panay, Senior Vice President of Devices and Services, the most meaningful AI moments happen side by side with family.

One of my favorite AI hacks right now is sitting down with my son and writing code together,

Panay says.

Starting from zero and creating something reminds you that AI isn’t just about consuming—it’s about building and learning together.

In the kitchen, AI becomes a collaborator.

I took a photo of my mother-in-law’s handwritten kibbeh recipe and uploaded it to Alexa,

he recalls.

Step by step, Alexa became my sous-chef.

The experience was more than practical.

It turned into a conversation about substitutions, techniques, and timing. It’s deeply emotional—it brings family, memory, and tradition to life.

Even reading has changed.

Kindle’s ‘Story So Far’ completely changed how I read,

Panay says.

It pulls you right back into the story and the characters you care about.

A More Human Future for AI

Across these stories, a common theme emerges: AI works best when it fades into the background and amplifies what matters most—time, connection, creativity, and presence.

As Panay puts it:

My advice? AI should be useful and keep you present. Try one simple thing—talk to Alexa about a family recipe, ask Kindle about a book you haven’t picked up in a while, or create something entirely new that inspires you.

Material by Iva Abadjievа

Source: Amazon

1. GitHub Copilot

GitHub Copilot works as an AI pair programmer embedded directly in your IDE. It suggests code in real time, generates functions from comments, and adapts to your coding style as you work. For many developers, it quickly becomes part of their daily workflow.

Best for: Everyday coding and autocomplete
Strength: Seamless IDE integration and broad language support

Pricing: Available as a paid subscription for individuals and teams, with discounted or free access for verified students and open-source maintainers. Enterprise pricing is offered through GitHub Enterprise plans.

• Individuals: from ~$10/month
• Business & Enterprise: from ~$19–$39/user/month
• Free for verified students and open-source maintainers

2. ChatGPT

ChatGPT is a conversational AI widely used by developers to generate code, debug issues, and reason through design decisions. It is not tied to a specific IDE, making it flexible across languages, frameworks, and platforms. Developers often rely on it as a thinking partner.

Best for: Debugging, explanations, and system design
Strength: Strong reasoning and versatility

Pricing: Offers a free tier with usage limits, alongside paid plans that provide access to more advanced models, higher usage caps, and faster responses. Team and enterprise plans are available for organizations.

• Free tier: $0 (usage limits apply)
• Plus plan: ~$20/month
• Team & Enterprise: custom pricing (typically $25+ per user/month)

3. Amazon CodeWhisperer

Amazon CodeWhisperer is optimized for cloud development, especially within the AWS ecosystem. It generates code aligned with AWS services and highlights potential security vulnerabilities. This makes it well suited for production-focused teams.

Best for: AWS and cloud-native development
Strength: Security-aware suggestions and AWS expertise

Pricing: Includes a free individual tier for basic usage, with paid professional and enterprise options offering enhanced security scanning and administrative controls.

• Individual tier: Free
• Professional: from ~$19/user/month
• Enterprise: custom AWS pricing

4. Tabnine

Tabnine provides AI-driven code completion with a strong focus on privacy. It supports local and private deployments, ensuring sensitive code never leaves controlled environments. This makes it a popular choice for enterprise and regulated teams.

Best for: Privacy-sensitive and enterprise environments
Strength: On-premise and private model support

Pricing: Offers a free tier with basic completion, plus paid Pro and Enterprise plans that unlock advanced models, team features, and private deployment options.

• $59/user/month (annual subscription)

5. Replit

Replit is a browser-based development environment that removes the need for local setup. Its AI features assist with coding, debugging, and collaboration in real time. Replit is widely used for learning, hackathons, and fast prototyping.

Best for: Learning, prototyping, and collaboration
Strength: Zero-setup, cloud-based workflow

Pricing: Provides a free tier with limited resources, while paid plans unlock more compute, private projects, and enhanced AI capabilities. Team and education plans are also available.

• Free tier: $0
• Replit Core: $20/month
• Teams: $40/user/month
• Enterprise: custom pricing

6. Cursor

Cursor is an AI-first code editor built around natural language interaction. Developers can ask questions about their entire codebase, refactor multiple files at once, and generate features using prompts. AI is central to the editor, not an add-on.

Best for: AI-native coding workflows
Strength: Deep codebase awareness and refactoring

Pricing: Typically offered via a subscription model, with a limited free trial and paid plans that increase usage limits and unlock advanced AI features.

• Hobby: $0/month
• Pro: $20/month

• Pro+: $60/month
• Ultra: $200/month

7. Sourcegraph Cody

Cody is designed to understand large and complex repositories. It helps developers search, explain, and modify code across massive codebases. This makes it particularly valuable for enterprise teams working with long-lived systems.

Best for: Large-scale and legacy codebases
Strength: Context-aware code intelligence

Pricing: Includes a free tier for individual developers, with paid Team and Enterprise plans that support larger codebases, self-hosting, and advanced integrations.

• $49/user/month

8. Windsurf

Windsurf offers fast AI-powered autocomplete and chat features across many IDEs. It is designed to be lightweight and easy to adopt without disrupting existing workflows. Many developers use it as a simple, high-performance alternative to heavier tools.

Best for: Fast autocomplete and low-friction adoption
Strength: Speed and broad IDE support

Pricing: Free for individual developers, with paid team and enterprise plans offering collaboration features, analytics, and administrative controls.

• Individual developers: Free
• Pro: $15/user/month
• Team: $30/user/month
• Enterprise: custom pricing

9. JetBrains AI Assistant

JetBrains AI Assistant is integrated directly into IDEs like IntelliJ IDEA, PyCharm, and WebStorm. It enhances code completion, refactoring, and documentation while leveraging JetBrains’ deep language analysis. The experience feels native and cohesive.

Best for: Developers using JetBrains IDEs
Strength: Native IDE experience and language intelligence

Pricing: Available as an add-on subscription, with some access bundled into select JetBrains plans. Pricing varies by individual, team, and organizational licenses.

• Add-on subscription: from ~$10/month
• Bundled access in select JetBrains plans
• Team & Enterprise licenses available

10. IBM watsonx Code Assistant

IBM watsonx Code Assistant focuses on enterprise software modernization. It helps teams document, refactor, and transform legacy systems while meeting governance and compliance requirements. The platform is designed for stability and long-term maintainability.

Best for: Enterprises and regulated industries
Strength: Governance, compliance, and legacy modernization

Pricing: Offered primarily through enterprise contracts, with pricing based on deployment size, use case, and compliance requirements rather than individual subscriptions.

• Essentials: Starting at approximately $2 per 20 task prompts.
• Standard: Starting at $3,000 per month
• On-premises: Upon request

11. Devin

Devin represents a new class of autonomous AI software engineers. It can plan tasks, write and debug code, run tests, and deploy applications independently. Rather than assisting line by line, Devin takes ownership of entire development tasks.

Best for: End-to-end task automation
Strength: Autonomous, multi-step execution

Pricing: Currently positioned as a premium product, typically available through limited access or enterprise-style pricing rather than open individual subscriptions.

• Core – $20/user/month
• Team – $500/month
• Enterprise – custom pricing

12. Claude

Claude is a reasoning-focused AI known for handling long and complex codebases. Developers use it to analyze unfamiliar systems, perform careful refactors, and generate clear documentation. Its cautious, structured approach makes it well suited for maintainable code.

Best for: Code understanding and refactoring
Strength: Long-context reasoning and clarity

Pricing: Offers free access with usage limits, alongside paid plans that provide higher limits, more advanced models, and team or enterprise options.

• Free tier: $0 (usage limits)
• Pro plan: from ~$20/month
• Max: from $100/person/month

Material by Iva Abadjievа

Image: Freepik

Images: GitHub Copilot; ChatGPT; Amazon CodeWhisperer; Tabnine; Replit; Cursor; Sourcegraph Cody; Windsurf; JetBrains AI Assistant; IBM watsonx Code Assistant; Devin; Claude

According to a report by 9to5Mac, Apple’s partnership with Google — first announced earlier this week — allows Apple to independently fine-tune the Gemini large language model so it responds in ways tailored to Apple’s products and branding, particularly in the upcoming Siri upgrade. The report also notes that Apple will avoid using Google or “Gemini” branding prominently within its Siri interface, even though Gemini is the underlying AI technology powering Apple Intelligence features that will run on Apple devices and its Private Cloud Compute infrastructure. This move indicates Apple’s attempt to balance leveraging cutting-edge AI with maintaining its distinctive product identity and privacy commitments.

Material by Iva Abadjievа