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April 30, 2025

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August 18, 2026

Valentina Ibinete, Marketing Lead at Kaizen Softworks

Valentina Ibinete

Travel magnet collector

Marketing Lead

AI

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Discover Windsurf Editor: The First AI-Powered IDE

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August 18, 2026

Last updated on

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August 18, 2026

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12

Valentina Ibinete, Marketing Lead at Kaizen Softworks

Valentina Ibinete

Marketing Lead

If you’re a software developer, you’ve probably used an Integrated Development Environment (IDE) like Visual Studio Code to write, test, and debug code.

Now imagine that same workspace, but with an intelligent assistant that does some of the work for you. That, in essence, is Windsurf. Let’s go over the essentials of this new AI tool. 

What is Windsurf Editor?

To give you a quick idea, Windsurf is like a Visual Studio Code pumped up with AI on steroids. Technically, it's a fork of VS Code, which means it takes the solid, open-source foundation of VS Code and adds new AI-powered functionalities. 

What Makes Windsurf Different?

Windsurf’s standout feature is its autonomous AI agent, called Cascade, that can understand natural language, perform complex coding tasks, and interact with your project. Think about having a virtual programming buddy you can talk to and ask to do things. 

Windsurf Editor user interface with Cascade AI agent visible, assisting with software development.

This agent not only understands your commands but is also capable of chaining together several complex actions to achieve your goals, like: 

  • Read library documentation: You can ask it to search for specific information on the web or even pass it a repository link.
  • Modify your code: It can help you refactor, add new features, or fix bugs.
  • Manage your environment: It can create new folders or execute commands directly in your terminal.

💡This autonomous agent isn't here to replace you as a programmer. Instead, it aims for an intelligent collaboration. You NEED to give them the context and instructions, MUST review its suggestions, and HAVE TO accept or decline the results. 

The Tools Behind the Agent

To carry out these complex tasks, Windsurf provides its autonomous agent with a series of specialized tools:

🔍 Search & Discovery

  • grep: Searches for text in files
  • listdir: Lists project folders and files
  • web search: Looks things up online
  • embedding search: Finds semantically related content, even if you don’t use the exact same words
  • MCP: Stands for Model Context Protocol. It allows the AI to safely use external tools (often built by third parties) by following a structured set of rules.

✏️ Editing & Modification

  • make edit: Makes inline code edits
  • add file: Creates new files
  • and other requests 

✅ Verification & Execution

  • Runs terminal commands
  • Executes tests or scripts to confirm things are working

How Does Windsurf Editor Work?

Windsurf’s workflow has three key parts:

  1. The Prompt: You give the instruction (prompt) to the agent through a conversational interface that can understand text, images, sound, and video.
  2. The Brain: The brain of Windsurf uses the LLM of your choice, interprets your instruction and determines what needs to be done.
  3. The Tools: Based on your prompt, the LLM learns to select and use the appropriate tools (read files, edit, search, etc.) to fulfill your request.

It's important to understand that LLMs are probabilistic, meaning that given the same instruction, they won't always generate the same response. 

In the context of programming, where we seek precise results, this highlights the importance of clear human guidance and careful review of what the agent proposes. If your instruction is ambiguous, the result could be either useful or completely wrong.

What Windsurf Can (and Can’t) Do

Windsurf runs on different large language models (LLMs), like the ones behind ChatGPT or Gemini. These are foundational models trained on massive amounts of data, giving them broad general knowledge. 

Windsurf Editor user interface with a dropdown menu or selection panel for choosing different AI models. Options include Open AI (ChatGPT), Anthropic (Claude), and Gemini (Google).

Each prompt uses "credits" that get renowned each month if you have a pro or enterprise plan. Be careful as more advanced models are more expensive and cost more credits per prompt.

However, they don’t know everything. If something wasn’t part of the training data, or if it happened after the model’s cutoff date (e.g., June 2024), the model won’t know it unless you tell it. This means it wont be able to use the latest version of some libraries so always ask the model "what’s the latest version of x" to double check. 

Also, no AI model fully understands the current state of your code or documents out of the box. That’s why Windsurf uses the tools mentioned above to explore your project and understand it, just like a developer would.

Windsurf relies heavily on context, meaning what you've asked before, what files it has seen, and any information you’ve shared in the session. But it doesn't "learn" new information permanently. For example, if you send it a link, it might use that content during the current conversation, but it won’t remember it afterward. It treats that as temporary context, not long-term memory.

Final Thoughts

Being among the first IDEs to fully integrate an autonomous AI agent, Windsurf offers a unique approach to coding. Used well, it can help you stay in flow, reduce context-switching, and explore solutions faster.

Ready to start programming with Windsurf? Check out our step-by-step guide and tips here.

If you’re a software developer, you’ve probably used an Integrated Development Environment (IDE) like Visual Studio Code to write, test, and debug code.

Now imagine that same workspace, but with an intelligent assistant that does some of the work for you. That, in essence, is Windsurf. Let’s go over the essentials of this new AI tool. 

What is Windsurf Editor?

To give you a quick idea, Windsurf is like a Visual Studio Code pumped up with AI on steroids. Technically, it's a fork of VS Code, which means it takes the solid, open-source foundation of VS Code and adds new AI-powered functionalities. 

What Makes Windsurf Different?

Windsurf’s standout feature is its autonomous AI agent, called Cascade, that can understand natural language, perform complex coding tasks, and interact with your project. Think about having a virtual programming buddy you can talk to and ask to do things. 

Windsurf Editor user interface with Cascade AI agent visible, assisting with software development.

This agent not only understands your commands but is also capable of chaining together several complex actions to achieve your goals, like: 

  • Read library documentation: You can ask it to search for specific information on the web or even pass it a repository link.
  • Modify your code: It can help you refactor, add new features, or fix bugs.
  • Manage your environment: It can create new folders or execute commands directly in your terminal.

💡This autonomous agent isn't here to replace you as a programmer. Instead, it aims for an intelligent collaboration. You NEED to give them the context and instructions, MUST review its suggestions, and HAVE TO accept or decline the results. 

The Tools Behind the Agent

To carry out these complex tasks, Windsurf provides its autonomous agent with a series of specialized tools:

🔍 Search & Discovery

  • grep: Searches for text in files
  • listdir: Lists project folders and files
  • web search: Looks things up online
  • embedding search: Finds semantically related content, even if you don’t use the exact same words
  • MCP: Stands for Model Context Protocol. It allows the AI to safely use external tools (often built by third parties) by following a structured set of rules.

✏️ Editing & Modification

  • make edit: Makes inline code edits
  • add file: Creates new files
  • and other requests 

✅ Verification & Execution

  • Runs terminal commands
  • Executes tests or scripts to confirm things are working

How Does Windsurf Editor Work?

Windsurf’s workflow has three key parts:

  1. The Prompt: You give the instruction (prompt) to the agent through a conversational interface that can understand text, images, sound, and video.
  2. The Brain: The brain of Windsurf uses the LLM of your choice, interprets your instruction and determines what needs to be done.
  3. The Tools: Based on your prompt, the LLM learns to select and use the appropriate tools (read files, edit, search, etc.) to fulfill your request.

It's important to understand that LLMs are probabilistic, meaning that given the same instruction, they won't always generate the same response. 

In the context of programming, where we seek precise results, this highlights the importance of clear human guidance and careful review of what the agent proposes. If your instruction is ambiguous, the result could be either useful or completely wrong.

What Windsurf Can (and Can’t) Do

Windsurf runs on different large language models (LLMs), like the ones behind ChatGPT or Gemini. These are foundational models trained on massive amounts of data, giving them broad general knowledge. 

Windsurf Editor user interface with a dropdown menu or selection panel for choosing different AI models. Options include Open AI (ChatGPT), Anthropic (Claude), and Gemini (Google).

Each prompt uses "credits" that get renowned each month if you have a pro or enterprise plan. Be careful as more advanced models are more expensive and cost more credits per prompt.

However, they don’t know everything. If something wasn’t part of the training data, or if it happened after the model’s cutoff date (e.g., June 2024), the model won’t know it unless you tell it. This means it wont be able to use the latest version of some libraries so always ask the model "what’s the latest version of x" to double check. 

Also, no AI model fully understands the current state of your code or documents out of the box. That’s why Windsurf uses the tools mentioned above to explore your project and understand it, just like a developer would.

Windsurf relies heavily on context, meaning what you've asked before, what files it has seen, and any information you’ve shared in the session. But it doesn't "learn" new information permanently. For example, if you send it a link, it might use that content during the current conversation, but it won’t remember it afterward. It treats that as temporary context, not long-term memory.

Final Thoughts

Being among the first IDEs to fully integrate an autonomous AI agent, Windsurf offers a unique approach to coding. Used well, it can help you stay in flow, reduce context-switching, and explore solutions faster.

Ready to start programming with Windsurf? Check out our step-by-step guide and tips here.

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·

Aug 28, 2026

About Catalyst 26: Partnerships & Ecosystem Conference

Everything to know about Catalyst 26: dates, price, who attends, both keynote recaps, and when the next Catalyst event is.

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Catalyst 26 was Partnership Leaders' fifth annual conference for partnership, ecosystem, and go-to-market professionals. It took place August 25 and 26, 2026, at the Marriott Hotel at the Brooklyn Bridge in New York, with more than 1,000 attendees and 70-plus speakers from companies including Anthropic, OpenAI, Google, Microsoft, IBM, BCG, and Siemens.

Dates August 25–26, 2026
Location Marriott Hotel at the Brooklyn Bridge, Brooklyn, NY
Edition 5th annual
Attendees 1,000+ partnership, ecosystem, and GTM professionals
Speakers 70+, including people from Anthropic, OpenAI, Google, Microsoft, IBM, BCG, and Siemens.
Price $849 early bird, rising to $999, then $1,999

Who Catalyst events are for

Catalyst brought together people building and running partner programs across SaaS, AI, consulting, systems integration, agencies, and major cloud platforms.

Attendees included executives leading partnership organizations, and people working directly in alliances, partner sales, marketing, operations, strategy, and enablement.

What Catalyst 26 is like

You can look at the agenda before a conference and have a pretty good idea of what you'll find. Being there is different.

This year's theme was "Navigating Frontier Ecosystems". Anthropic's Head of Partnerships and one of OpenAI's partner program leads appeared on the same agenda as people from Oracle, Siemens, IBM, and BCG, companies that have run formal partner programs for two decades.

That mix was one of the most interesting parts of the conference. Newer AI companies were discussing partner tiers, co-selling, and joint delivery alongside companies where those models have been part of their business for years.

What Catalyst 26 covered

Catalyst 26 split its sessions into eight pillars:

  • Advancing Organizational Maturity: turning partnerships into something measured and repeatable instead of one founder doing favors for another.
  • Become a Strategic Partner: getting partnerships involved when product and business decisions are made, not told about them afterward.
  • Frontier Partner Experience: adapting partner programs as AI changes how companies build and integrate products.
  • Path to CPO: career sessions for people aiming to lead partnerships at the executive level.
  • Co-Build: two companies building something together.
  • Co-Market: two companies running a campaign together.
  • Co-Sell: two sales teams working the same deal.
  • Co-Serve: two companies delivering the same engagement to a client.

Catalyst 26 sessions

Day 1 Keynote

The Day 1 keynote brought together Partnership Leaders’ CEO Asher Mathew, Tribe AI’s Co-founder & CEO Jaclyn Rice Nelson, Anthropic’s Head of Partnerships Phil Samenuk, and Boomi’s Chairman & CEO Steve Lucas.

Their discussion focused on how companies are relying on partners to build, sell, and deliver products across AI, cloud, and enterprise software. A few points stood out:

  • More companies have dedicated partner teams now, which means a generic, one-size-fits-all partner program doesn't cut it anymore. Partners show up when the program fits how they work.
  • New AI products and cloud services are shipping so fast that a partner program can't just get set once and left alone. Incentives, support, and how you work together need regular updates.
  • Partnerships also came up as a way to access data a company couldn’t reach on its own, whether that meant getting access to it, combining it, or putting it to use.
  • AI doesn't change the basics of a good partnership. Account planning, clear ownership, and relationships built over time still matter most.

Day 2 Keynote

The Day 2 keynote featured Ramp’s Lead Economist Ara Kharazian, Eliza’s Founder Brian Benedict, Siemens’ EVP Global Partner Ecosystem Dion Smith, and Oracle’s SVP, Partner Sales & Operations Strategy Leah Yomtovian.

A few points stood out:

  • The spending data told a slower story than expected: AI adoption is mostly going toward productivity gains and task automation, not some overnight shift.
  • Siemens is in the middle of folding more than 68,000 partners and roughly 200 separate programs into a single global one, mainly to make it easier to coordinate across IT and operational technology.
  • Oracle's approach is a running "listening tour": every partner gets the same baseline benefits, then incentives and credits get layered based on the type of partner and how they work with Oracle.
  • There was also talk of a newer kind of service team: bring in engineers, turn AI requirements into working products, and reuse delivery methods that already work instead of starting from scratch each time.

Next Catalyst events

The date and location of Catalyst 27 hasn’t been announced yet. In the meantime, you can check out the half-day Catalyst Summits in different cities:

  • October 20, 2026 - Seattle
  • October 27, 2026 - Chicago
  • October 2026 - Los Angeles
  • December 2026 - Singapore

Check Partnership Leaders’ events page for updates.

·

Aug 26, 2026

Why adding people doesn't always fix a struggling team

Learn when a software team should hire, wait, reorganize, or build skills internally, and how to tell which option will actually help.

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When a client asks to hire someone new, a common reaction is to open a search. There's more work, more pressure, and new features to build. It seems like the obvious thing to do.

But in our experience working with software development teams, the problem often isn't a lack of people. The problem is knowledge concentrated in too few people, unclear team roles, slow onboarding, or temporary demand.

The question worth asking isn't who can fill the position, but what would help the team work better. That points to one of three answers: hire, don't hire, or build the capability from within. Figuring out which one applies, and why, is the real work before opening a search.

What you should ask before assuming you need someone new

Hiring works when three conditions are met: the need will last, no one on the team has the capacity to take it on, and the team can onboard someone well. That last condition is easy to overlook. A team can have a real, lasting gap and still not be ready to bring someone in if no one has the time to guide them.

The risk comes from jumping straight from "there's more work" to "we need someone" without checking what's causing the pressure. It's easy to turn a request into a list of requirements (X years of experience, a specific technology, advanced English) and start the search. The real cause is often something else: a project that grew too fast, a tech lead with no time to onboard new hires, processes that stopped scaling, or a team that lost key people and needs to recover knowledge before adding headcount.

That's why, before thinking about who could fill the role, we ask these questions:

  • What outcome is the client trying to achieve?
  • What's happening on that team today?
  • What specific problem is this hire meant to solve?
  • Does adding a person solve that problem?
  • Is there someone on the team who could take this on?
  • Are there other, less obvious alternatives?

When the answers confirm the need will last, the current team can't cover it, and the team has the capacity to onboard someone, hiring is the right call: opening the search fills a gap the team can't close internally.

Does the problem need someone new to fix it?

Not hiring is the right call when the problem behind the request is temporary, or when it will resolve before the new hire finishes onboarding. Recommending against a hire may sound unusual for a company that offers staff augmentation, but our job as a strategic partner is not to maximize every opportunity but to recommend the best decision for the client. Depending on what's actually going on, the fix can look like:

  • An internal rotation: moving someone with spare capacity into the gap.
  • Reorganizing responsibilities across the team instead of adding a seat.
  • Hiring a different profile than the one originally requested.
  • Combining two roles into one instead of opening two searches.
  • Waiting a few weeks, when the project context is about to change on its own.

Is a temporary increase in workload a good reason to hire?

This happened on a project with a long onboarding period. The initial request seemed clear: hire a mid-level developer. There was work and budget available. But when we spoke with the team, we found that the workload increased because one team member had been temporarily reassigned to another sub-team. Before moving forward, we considered what would happen when that person came back.

The client's system was complex: any new hire needed several months to understand the business, the architecture, and the platform before they could contribute independently.

The problem justifying the hire was going to disappear, but the new hire wouldn't. By the time that person had enough context, the need that started the search would no longer exist.

We recommended against moving forward, even though there was budget to add someone. The client avoided an unnecessary hire and months of onboarding for a problem that was already resolving itself. Sometimes the best answer is to wait a few weeks; other times, it's reorganizing the team or developing internal talent.

How can you build team capability without hiring?

Build capability internally when the team already has product context but lacks a specific skill. Developing that skill internally can be faster than waiting for someone new to reach the same level of context.

More people doesn't always mean more capacity. Onboarding a new hire takes time from the people already on the team: explaining the business and the architecture, reviewing their work, and building trust. That's why, during the first few weeks, a team can become less productive while it onboards someone new. Complex projects can include years of technical decisions and undocumented knowledge. New hires still need time to learn that context.

Should you hire a specialist or train someone on your team?

A client needed a senior SQL Server specialist. That niche skill set made the role difficult and expensive to fill. We started the search and interviewed candidates, but the deeper issue became clear quickly: the real challenge on the project wasn't SQL Server. It was understanding a product shaped by years of evolution, multiple applications, and complex business logic.

The right person to develop that expertise was already on the team. Instead of hiring someone with deep SQL Server expertise, the client supported that team member in building the SQL Server skills the project needed. That person had business knowledge, motivation, and a much shorter learning curve than an external hire would have had. An outside specialist provided targeted support when needed.

The team gained SQL Server expertise without losing months waiting for a new hire to learn the product first. The person who took on SQL Server gained a valuable new skill without stepping away from the other work they were doing on the project.

A team's capacity depends on how its people complement each other, what knowledge they share, and what autonomy they've developed, not just on headcount. A team of ten people who are aligned, with shared context and autonomy, can generate more value than a team of fifteen where much of the time goes into onboarding new hires.

Should you hire, wait, or develop the skill internally?

Scenario Signal What to do
Hire The need will last, no one on the team can cover it, and the team can onboard someone well Open the search for a clearly defined role
Don't hire The problem is temporary or resolves before onboarding finishes Wait, reorganize the team, or cover the gap another way
Build internal capability Missing specific expertise, not people; someone already has the business context Develop the skill internally, with targeted outside support if needed

What questions do we ask first?

  1. What specific problem are we trying to solve?
  2. Will the need still exist after the person has been hired and onboarded?
  3. Is there someone on the team who could cover it?
  4. Do we have the capacity to onboard someone well?
  5. Is the problem a lack of people, or is it caused by unclear roles, missing product knowledge, slow onboarding, or a temporary increase in workload?
  6. What impact will this hire have six months from now?
  7. If we couldn't hire today, what other option would we explore?
  8. What higher-priority work would someone on the team have to stop doing to cover this need?

Wait to open a search when the team can't define the problem, confirm the need will last, or support onboarding. Clarify those points first.

If you're weighing this decision with your own team, let's talk about whether to hire, reorganize, or develop someone already on the team.

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