I’ve been tracking AI tools closely for the past two years, and 2026 has been genuinely difficult to keep up with. So here’s my honest take on the five tools that have most changed how I work, and what I’ve actually found each one useful for.
1. Claude by Anthropic
I use Claude daily and have for over a year. What’s changed most in 2026 is reliability. It hallucinates less, follows complex multi-step instructions better, and handles the kind of ambiguous input that used to trip up earlier models.
The extended context window (up to 200K tokens) is useful in practice, not just as a spec-sheet number. I’ve fed it entire codebases, full research documents, and long conversation histories and had it reason coherently across all of it. For research, long-form writing, code review, and agent pipelines, it’s my default starting point.
Honest caveat: the quality of what Claude produces is heavily dependent on how well you prompt it. Vague inputs get mediocre outputs. Specific, well-structured inputs get remarkably good results. The tool rewards investment in how you use it.
2. Cursor
I switched my main editor to Cursor about a year ago and haven’t gone back. It’s a fork of VS Code that wraps AI deeply into the editing experience rather than bolting it on as an extension.
The part I find genuinely useful: it understands your full codebase, not just the open file. Ask it to refactor something and it knows what the refactor needs to touch elsewhere. The Composer mode lets you describe a change in plain language and watch it get applied across multiple files. For AEM work and enterprise Java projects, having that cross-file context has saved me real time on things that used to be tedious archaeology.
It still makes mistakes and you’re still reviewing everything. But the baseline competence is high enough that it’s changed how I approach development work, not just how fast I get through it.
3. Sora by OpenAI
The 2025 version of Sora generates video that’s genuinely hard to distinguish from professionally shot footage, at least for short clips. For marketing teams, the economics are compelling: dramatically lower production costs, faster iteration, custom styles and aspect ratios on demand.
I haven’t used it heavily for my own work, but I’ve seen what teams using it are producing and it’s good. Good enough that it’s changing production workflows rather than just supplementing them. The broader implications for the video production industry are a longer conversation.
4. GitHub Copilot
This is probably the tool that’s changed how I approach development work the most alongside Cursor. With its agent mode, Copilot takes a plain-English description of what you want, plans an implementation, writes code across multiple files, and runs tests. You review and steer throughout. It’s come a long way from the tab-completion tool it started as.
What I’ve noticed: it understands full repository context, not just the current file. That means it can make architecturally coherent changes, not just locally correct ones. Multi-file refactoring, test generation, and documentation are all meaningfully faster. The developers getting the most from it are the ones who already have strong opinions about what good architecture looks like.
5. Perplexity AI Pro
I resisted Perplexity longer than I should have. It felt like a formatted search engine. Then I used it seriously for research and realized I’d been underselling it.
Real-time web access with citations inline has replaced a significant portion of my Google search-and-scan workflow. The deep research mode generates multi-page synthesis reports on complex topics that are genuinely useful starting points. Integrations with Notion and Slack mean it fits into existing workflows rather than requiring you to build new ones around it.
Where it still has limits: anything requiring deep reasoning beyond the source material. For that, you want Claude or a frontier reasoning model. But for research, especially on recent developments and fast-moving topics, Perplexity is the right tool.
The honest takeaway
None of these tools work in isolation, and none of them work without you doing the thinking. What I keep seeing is that people who get real value from AI tooling bring genuine expertise and judgment. They use AI to accelerate execution, not to avoid having to think. The people who are disappointed are usually treating these as magic answer machines. They’re not.
The stack I use: research with Perplexity, building with Cursor and Copilot, reasoning and writing with Claude. Sora where the use case fits. It’s an ecosystem, not a single tool, and it rewards having a clear sense of what each one is good at.