DEV Community: Anshul Rajpal

Why I Stopped Using Docker for Local Development

Anshul Rajpal — Mon, 12 Oct 2026 09:23:07 +0000

The Breaking Point

I used Docker for three years. It was the default for every project, every tutorial, every team I joined. Then one day I spent 45 minutes debugging a Docker networking issue instead of writing code. That was the breaking point.

The problem wasn't Docker itself. The problem was that I was using Docker for everything, even when I didn't need it. Local development on my machine became slower, not faster. Every change required a rebuild. Every debug session required checking container logs. Every new team member required a 30-minute Docker setup.

I decided to try something different: running everything natively on my machine.

What I Switched To

I run Node.js, Python, PostgreSQL, and Redis directly on my Windows machine. No containers. No virtual machines. Just native processes.

For Node.js, I use nvm-windows to manage versions. For Python, I use pyenv-win. For databases, I run them as Windows services. For Redis, I use Memurai (a Windows-native Redis-compatible server).

The setup took about 2 hours. The first project took 10 minutes to get running. No Dockerfiles. No docker-compose.yml. No container networking.

What I Gained

Speed. Starting my dev environment takes 30 seconds. Not 5 minutes. Not 10 minutes. 30 seconds. I open my terminal, run two commands, and I'm coding.

Debugging. When something breaks, I can attach a debugger directly to the process. No container logs. No "docker exec" to check what's happening. Just attach and debug.

Disk space. I freed up 40GB of disk space. Docker images, volumes, and build caches were eating my SSD.

Mental energy. I stopped thinking about Docker. I stopped debugging container issues. I stopped explaining to new team members why their ports don't work. I just write code.

What I Lost

Isolation. If I break something, I break it on my machine. No container to throw away and recreate. I had to learn to be more careful with my dependencies.

Reproducibility. "Works on my machine" is now literally true. If I set up a new machine, I need to install everything manually. No docker-compose up to get started.

Team consistency. If the team uses Docker, I need to either use Docker or make sure my native setup doesn't break anything. This is the hardest part.

What I'd Do Differently

Start with native development first. Learn how your tools work without containers. Then add Docker when you actually need it - for deployment, for CI, for team consistency.

Don't use Docker for everything. Use it for what it's good at: packaging, deployment, and running services that are hard to install natively.

Don't be afraid to go against the grain. Just because everyone uses Docker doesn't mean you have to.

The Real Lesson

Docker is a tool, not a religion. Use it when it helps. Don't use it when it doesn't. The best tool is the one that lets you write code without thinking about the tool.

I think about Docker maybe once a month now. That's exactly how much I want to think about it.

If you're spending more time debugging Docker than writing code, it might be time to try something different.

What I Learned About Myself

Going native taught me that I was using Docker as a crutch. I didn't understand how my tools worked because Docker abstracted everything away. When something broke, I had no idea how to fix it. I just rebuilt the container and hoped for the best.

Running everything natively forced me to learn. I learned how Node.js resolves dependencies. I learned how PostgreSQL handles connections. I learned how Redis persists data. I learned how my operating system manages processes.

That knowledge is worth more than the convenience of Docker. When something breaks now, I can actually fix it. I don't have to wait for a container to rebuild or hope that restarting Docker Desktop fixes the issue.

The Mistake Everyone Makes

The biggest mistake I see developers make with Docker is using it as a learning avoidance tool. They don't want to learn how to install PostgreSQL, so they use a Postgres container. They don't want to learn how to configure Redis, so they use a Redis container. They don't want to learn how their tools work, so they containerize everything.

Then when something breaks, they're stuck. They can't debug it because they don't understand it. They can't fix it because they never learned how it works.

Docker is great for deployment. It's great for CI. It's great for running services that are hard to install. But it's not a substitute for understanding your tools.

If You're Starting Today

Don't start with Docker. Start native. Learn how your tools work. Learn how to install them, configure them, and debug them. Then add Docker when you actually need it.

Don't use Docker for everything. Use it for what it's good at: packaging, deployment, and running services that are hard to install natively.

Don't be afraid to go against the grain. Just because everyone uses Docker doesn't mean you have to. The best setup is the one that lets you write code without fighting your tools.

The best development environment is the one you understand. Mine is native now. That's the only metric that matters.

I Built a Second Brain With Obsidian - Here is What Actually Worked

Anshul Rajpal — Fri, 09 Oct 2026 12:19:55 +0000

The Mess That Started It All

Six months ago, my notes were scattered across seven apps. Google Keep for quick captures, Notion for projects, a random folder of markdown files, three different journaling apps, and a notes app I forgot the password to. I had thousands of notes and could find none of them.

The breaking point was a meeting where someone asked about a decision I made three months prior. I knew I'd written it down. I knew it was somewhere. It took me 20 minutes of searching across apps to find it. That's when I decided: enough.

I'd been watching people on Twitter show off their "second brain" setups - beautiful Obsidian graphs with hundreds of connected notes, color-coded tags, daily notes stretching back years. It looked like art. It also looked like a full-time job.

I didn't want art. I wanted to find my notes.

The Setup I Actually Used

I started with the P.A.R.A. method - Projects, Areas, Resources, Archives. Four folders. That's it. No complex taxonomy, no nested folder hierarchies that would collapse under their own weight.

Projects: active things with deadlines. Areas: ongoing responsibilities without end dates. Resources: reference material I might need later. Archives: completed or inactive stuff.

The key insight: most note-taking systems fail because they optimize for capture, not retrieval. I optimized for retrieval. Every note had to answer one question: "When will I need this again?"

If I couldn't answer that, it didn't go in.

What Broke in the First Month

The graph view broke me. Obsidian shows your notes as a connected web, and it's addictive. I spent an entire weekend linking notes, adjusting tags, making the graph look pretty. Then I realized: I'd spent 8 hours on zero actual thinking.

The daily notes feature broke me too. I tried journaling every day. Lasted 11 days. The pressure to write something meaningful every single day turned a useful tool into a guilt machine.

The plugin ecosystem broke me. There are 2,000+ Obsidian plugins. I installed 40 of them. Each one added complexity. Each one was a small tax on every interaction. I uninstalled 35 and kept only Dataview, Templater, and Kanban.

What Actually Worked

The inbox pattern. One note called "Inbox." Everything goes there first. Once a week, I process it - file notes into P.A.R.A., delete junk, extract action items. The inbox is allowed to be messy. Nothing else is.

Atomic notes. One idea per note. Not one topic per note - one idea. "The P.A.R.A. method uses four folders" is a note. "P.A.R.A. method" is not. Small notes link together. Big notes become unreadable.

The "why" tag. Every note gets a tag explaining why I'm saving it. #why/reference, #why/decision, #why/learning. When I search later, I can filter by why I saved something, not just what it's about.

Weekly review. Every Sunday, 30 minutes. Process the inbox, review recent notes, update project statuses. This is the engine that keeps the system running. Without it, the system decays in about two weeks.

The Numbers After 6 Months

847 notes (down from 3,000+ scattered across apps)
Average time to find a note: under 10 seconds
Weekly review consistency: 80% (missed about 5 weeks)
Plugins installed: 3
Graph view opens per month: maybe 2

The graph view is still pretty. I just don't use it for thinking anymore.

What I'd Do Differently

Start with fewer notes. I imported everything from my old apps. Most of it was junk. I should have started empty and added only what I actually use.

Don't optimize the system before using it. I spent two weeks setting up templates, workflows, and automation before writing a single real note. The system should emerge from use, not precede it.

Use the mobile app more. The desktop app is great, but most of my ideas come when I'm not at my desk. The mobile app is where capture happens.

The Real Lesson

A second brain isn't about having perfect notes. It's about having a system you actually use. The best system is the one that's still running after 6 months.

Mine is still running. Not because it's perfect - because it's simple enough that I don't hate using it.

If you're thinking about building a second brain, start with one folder and one note. Add structure only when the pain of not having it becomes real. Not before.

The pretty graphs can wait.

What I Learned About Myself

Building a second brain taught me more about how I think than any productivity book ever did. I learned that I capture ideas best when I'm walking or driving - not when I'm at my desk. So I set up a voice memo shortcut on my phone that transcribes and drops notes into my Obsidian inbox.

I learned that I'm a "just-in-case" saver. I save things I might need someday, even if I probably won't. The "why" tag helped me be honest about this. If I can't articulate why I'm saving something, I don't save it. This alone cut my note volume by 60%.

I learned that I think in connections, not categories. The graph view isn't just pretty - it's how my brain works. But I don't need to look at it every day. Once a week is enough to surface unexpected connections.

The Mistake Everyone Makes

The biggest mistake I see people make with second brains is treating it like a museum. They curate it, polish it, make it look perfect. Then they stop using it because they don't want to mess it up.

A second brain is a workshop, not a museum. It's supposed to be messy. Notes get rewritten. Links get broken. Tags get reorganized. That's not failure - that's the system working.

My system has changed 4 times in 6 months. Each time, I thought I'd found the "final" version. Each time, I was wrong. The system evolves as I evolve. That's the point.

If You're Starting Today

Don't import your old notes. Start empty. Add only what you actually use.

Don't install plugins. Use the app plain for 30 days. Add plugins only when you hit a real limitation.

Don't journal daily. Journal when you have something to say. Forced daily entries become garbage.

Don't optimize the graph. Optimize retrieval. If you can find what you need in under 10 seconds, your system is working.

And most importantly: don't compare your system to someone else's. Their second brain is built for their brain. Yours needs to be built for yours.

The best second brain is the one that's still running after 6 months. Mine is. That's the only metric that matters.

Developer Burnout in the AI Era: It's Not What You Think

Anshul Rajpal — Thu, 08 Oct 2026 20:44:48 +0000

The Burnout That Didn't Go Away

We were promised that AI tools would save us. Write less code, ship faster, work less. And in some ways, they did. I can generate boilerplate in seconds, auto-complete entire functions, and debug faster than ever.

But burnout didn't go away. It just changed shape.

I've been a developer for years. I've seen the crunch times, the on-call rotations, the impossible deadlines. But this is different. This is a new kind of exhaustion that nobody warned me about.

The Verification Burden

Here's what nobody tells you about AI coding tools: they don't eliminate work, they transform it. You spend less time writing code, but you spend more time verifying it. Reviewing AI-generated code, checking for subtle bugs, making sure the logic actually makes sense.

It's like having a junior developer who works incredibly fast but needs constant supervision. Except you can't delegate the supervision — it's all on you.

I found myself spending 4-5 hours in extremely intense code review sessions before my brain was completely fried. The output was faster, but the cognitive load was heavier. You're not writing code anymore, you're auditing it. And auditing is exhausting in a different way.

The Productivity Pressure

Management heard the headlines: "AI makes developers 2x more productive." So now they expect 2x output. But the reality is more nuanced. Yes, you can generate code faster. But you still need to understand it, test it, deploy it, and maintain it.

The gap between expectation and reality is where burnout lives.

I've seen teams where the pressure to "keep up with AI" created a new kind of anxiety. Developers feel like they're falling behind if they're not using every AI tool available. The tools that were supposed to reduce stress became another source of pressure.

The Identity Crisis

Here's the part that's harder to talk about. For many of us, our identity as developers was tied to writing code. The craft of it. The problem-solving. The satisfaction of building something from nothing.

When AI generates the code, where does that leave you?

I've felt this myself. There's a weird disorientation when you realize you're spending more time reviewing code than writing it. The pride of craftsmanship gets replaced by the anxiety of verification. You're still essential, but your role has shifted in ways that aren't always comfortable.

What Actually Helps

After months of struggling with this, here's what I've learned actually helps:

Set boundaries with AI tools. Don't use them for everything. Some tasks are better done manually. The tool should serve you, not the other way around.

Protect your learning time. AI tools change fast. If you're not keeping up, you'll feel left behind. But learning needs to be protected time, not something you squeeze in after hours.

Talk about the pressure. The worst thing about this new burnout is that it's invisible. Everyone looks productive on the surface. But underneath, there's a lot of stress. Talking about it helps.

Remember that slower is sometimes better. Real productivity isn't about how much code you ship this week. It's about whether your team is still healthy enough to ship code next year.

The Bottom Line

AI tools are genuinely useful. They've made me faster in many ways. But they haven't solved burnout, they've just changed its shape.

The exhaustion of 2026 isn't from lacking tools. It's from carrying new responsibilities while being held to standards that assume the tools eliminate work rather than transform it.

If you're feeling this, you're not alone. And it's not a personal failure. It's a systemic issue that we need to talk about more openly.

The tools are here to stay. The question is: how do we use them without burning out?

Agentic AI: The Real Shift Is Multi-Agent Systems

Anshul Rajpal — Thu, 08 Oct 2026 10:15:17 +0000

What Is Agentic AI

Agentic AI is not a new model. It is a new way of using models. Instead of asking one AI to do everything, you break a task into pieces and let multiple agents work on different parts simultaneously.

The idea is simple: one agent plans, another codes, a third reviews. They coordinate through a central orchestrator that delegates tasks and synthesizes results. No single model needs to be perfect at everything — each agent specializes in one thing.

I tested multi-agent systems for a week across Claude Code, Muse Code, and Google's AX orchestrator. Here is my honest review.

Key Features

Multi-agent systems ship with several features that make them stand out:

Task decomposition — break a large task into smaller subtasks and assign each to a dedicated agent.
Parallel execution — multiple agents work on different parts of the same task simultaneously.
Agent communication — agents can message each other to share context and coordinate.
Central orchestration — a single orchestrator manages the swarm, handles failures, and synthesizes results.
Specialization — each agent focuses on one thing and gets better at it over time.

What I Liked

The task decomposition is genuine. I used Claude Code to break a large refactoring task into smaller subtasks. Each subtask was assigned to a dedicated agent that worked independently. The results were noticeably better than using a single agent for the entire task.

The parallel execution is the real star. Multiple agents working on different parts of the same task simultaneously saved me hours compared to sequential execution. The key is that you have the choice, not the orchestrator.

I tested this by running the same task with different agent configurations. The quality difference was noticeable — specialized agents produced better code than a general-purpose agent handling everything. The key is that you have the choice, not the orchestrator.

The agent communication is impressive. Agents can message each other to share context and coordinate. This is a game-changer for complex tasks that require multiple steps.

Where It Broke

Multi-agent systems are not perfect. The orchestrator is smart but not infallible. I had a few cases where the wrong agent was picked for the task. The auto agent feature picks the agent based on the task description, but it is not always accurate. Manual agent selection is more reliable but defeats the purpose of auto-routing.

The task decomposition is impressive but not perfect. I found that the orchestrator sometimes breaks tasks into too many small pieces. This creates overhead and slows down the overall process. Manual decomposition is more reliable but defeats the purpose of auto-decomposition.

The agent communication is impressive but not perfect. I found that agents sometimes send redundant messages. This creates noise and makes it harder to track what is happening. Manual coordination is more reliable but defeats the purpose of agent communication.

Who This Is For

Developers who use AI coding agents daily and want model flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to Cursor or Claude Code
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code

Common Questions

Q: Is agentic AI free?

A: The client is free and open source under MIT. You pay the model provider's rate for inference. Card credit purchases carry a 5% processing fee.

Q: How does agentic AI compare to single-agent systems?

A: Single-agent systems are more polished for simple tasks but lock you into one model. Agentic AI gives you multi-agent support and zero markup. Single-agent systems have a better UX for casual users; agentic AI is better for power users who want control.

Q: Can I use local models?

A: Yes. Agentic AI supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

Agentic AI is a genuine shift in how we use AI. Task decomposition, parallel execution, agent communication — these are not marketing tricks.

If you are tired of being locked into a single model or provider, try agentic AI. The free tier is generous enough to test without spending a dime.

The orchestrator needs work, and the agent communication can be noisy. But for developers who want model flexibility and cost control, agentic AI is one of the best options out there.

AlphaCode: The Free MIT AI Coding Agent Built in Rust

Anshul Rajpal — Wed, 07 Oct 2026 21:45:25 +0000

What Is AlphaCode

AlphaCode is a free MIT AI coding agent built in Rust. It launched in 2026 and has been quietly gaining traction among developers who want a terminal-based AI coder that works with Claude, GPT, Gemini, and DeepSeek models.

The pitch is simple: one CLI, any model, zero markup. You bring your own keys, pick the model, and AlphaCode routes the request. No silent model switching. No vendor lock-in. No surprise bills.

I tested AlphaCode for a week across Claude Code, Codex, and the CLI. Here is my honest review.

Key Features

AlphaCode ships with several features that make it stand out:

Terminal-first — run AI coding agents from the command line. No IDE needed. Just a terminal and your favorite model.
Multi-model support — works with Claude, GPT, Gemini, DeepSeek, and any OpenAI-compatible model. Switch models mid-task without losing context.
Built-in free model — AlphaCode has a built-in free model. You can use it without any API key.
Swarm mode — run multiple agents in parallel for code review and planning.
40+ tools — browser automation, desktop automation, terminal commands, and more.

What I Liked

The terminal-first approach is genuine. I used AlphaCode from the CLI for code generation, debugging, and code review. It works well for quick tasks and long-running sessions alike.

The built-in free model is the real star. AlphaCode gives you a free model that works without any API key. This is a game-changer for developers who want to test AI coding agents without spending a dime.

I tested this by running the same task with the free model and with Claude. The quality difference was noticeable — Claude produced better code, but the free model was fast enough for simple tasks. The key is that you have the choice, not AlphaCode.

The swarm mode is impressive. You can run multiple agents in parallel for code review and planning. The orchestrator mode handles parallel execution behind the scenes.

Where It Broke

AlphaCode is not perfect. The swarm mode is smart but not infallible. I had a few cases where the wrong agent was picked for the task. The auto agent feature picks the agent based on the task description, but it is not always accurate. Manual agent selection is more reliable but defeats the purpose of auto-routing.

The built-in free model is generous but not perfect. I found that the free model sometimes produces lower-quality code than Claude. For complex refactoring tasks, the free model can introduce errors. You need to review the output carefully when using the free model.

The multi-model routing is impressive but not perfect. I found that AlphaCode sometimes picks a model that is too weak for the task, especially for complex refactoring. Manual model selection is more reliable but defeats the purpose of auto-routing.

Who This Is For

Developers who use AI coding agents daily and want model flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to Cursor or Claude Code
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code

Common Questions

Q: Is AlphaCode free?

A: The client is free and open source under MIT. You pay the model provider's rate for inference. Card credit purchases carry a 5% processing fee.

Q: How does AlphaCode compare to Claude Code?

A: Claude Code is more polished for IDE integration but locks you into Anthropic models. AlphaCode gives you multi-model support and zero markup. Claude Code has a better UX for casual users; AlphaCode is better for power users who want control.

Q: Can I use local models?

A: Yes. AlphaCode supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

AlphaCode is a genuine open-source alternative to the big AI coders. Terminal-first, multi-model support, zero markup — these are not marketing tricks.

If you are tired of being locked into a single model or provider, try AlphaCode. The free tier is generous enough to test without spending a dime.

The swarm mode needs work, and the built-in free model can produce lower-quality code. But for developers who want model flexibility and cost control, AlphaCode is one of the best options out there.

OpenCode: The OSS CLI That Beats Paid AI Coders

Anshul Rajpal — Mon, 05 Oct 2026 09:29:01 +0000

What Is OpenCode

OpenCode is an open-source AI coding CLI with 167k stars. It launched in 2026 and has been quietly gaining traction among developers who want a terminal-based AI coder that works with Claude, GPT, and Gemini models.

The pitch is simple: one CLI, any model, zero markup. You bring your own keys, pick the model, and OpenCode routes the request. No silent model switching. No vendor lock-in. No surprise bills.

I tested OpenCode for a week across Claude Code, Codex, Cursor, OpenCode, Cline, and Copilot. I used it for code generation, debugging, code review, and planning. Here is what I found, the good, the bad, and the ugly.

Key Features

OpenCode ships with several features that make it stand out:

Terminal-first, run AI coding agents from the command line. No IDE needed. Just a terminal and your favorite model.
Multi-model support, works with Claude, GPT, Gemini, and any OpenAI-compatible model. Switch models mid-task without losing context.
VS Code integration, OpenCode has a VS Code extension for in-editor AI assistance.
MCP support, OpenCode supports Model Context Protocol. This means you can use OpenCode with any tool that supports MCP.
Zero markup, you pay the model provider's rate directly. No markup, no hidden fees.

What I Liked

The terminal-first approach is genuine. I used OpenCode from the CLI for code generation, debugging, and code review. It works well for quick tasks and long-running sessions alike.

The multi-model support is the real star. OpenCode routes requests to Claude, GPT, Gemini, and any OpenAI-compatible model through a single unified endpoint. You can switch models mid-task without losing context.

I tested this by running the same task through different models. The quality difference was noticeable, Claude and GPT produced better code, but the cheap models were fast enough for simple tasks. The key is that you have the choice, not OpenCode.

The VS Code extension is a big deal. You can use OpenCode from your terminal or install the VS Code extension for in-editor AI assistance. The extension integrates seamlessly into your existing workflow.

I installed OpenCode on VS Code, JetBrains, and the CLI. The installation took less than 5 minutes on each platform. The extension detected my existing AI agents and configured them automatically.

Where It Broke

OpenCode isn't perfect. The VS Code extension is a ground-up rebuild and still feels rougher than the CLI. Some features that work in the CLI are missing or incomplete in the VS Code extension.

The MCP support is smart but not infallible. I had a few cases where OpenCode failed to connect to MCP servers. The MCP integration works well for simple tools, but complex MCP servers sometimes fail to connect.

The multi-model routing is impressive but not perfect. I found that OpenCode sometimes picks a model that is too weak for the task, especially for complex refactoring. Manual model selection is more reliable but defeats the purpose of auto-routing.

I also tested the MCP support by running OpenCode with multiple MCP servers. The MCP integration works well for simple tools, but complex MCP servers sometimes fail to connect.

This is a common issue with AI coding agents, not just OpenCode. But it is worth being aware of before you rely on the MCP integration for critical tasks.

Who This Is For

Developers who use AI coding agents daily and want model flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to Cursor or Claude Code
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code
Developers who work with multiple models and want a unified interface
Teams who need to run multiple agents in parallel without managing multiple API keys

Common Questions

Q: Is OpenCode free?

A: The client is free and open source under MIT. You pay the model provider's rate for inference. Card credit purchases carry a 5% processing fee.

Q: How does OpenCode compare to Claude Code?

A: Claude Code is more polished for IDE integration but locks you into Anthropic models. OpenCode gives you multi-model support and zero markup. Claude Code has a better UX for casual users; OpenCode is better for power users who want control.

Q: Can I use local models?

A: Yes. OpenCode supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

OpenCode is open-source alternative to the big AI coders. Terminal-first, multi-model support, zero markup, these aren't marketing tricks.

If you are tired of being locked into a single model or provider, try OpenCode. The free tier is generous enough to test without spending a dime.

The VS Code extension needs work, and the MCP integration can fail on complex servers. But for developers who want model flexibility and cost control, OpenCode is one of the best options out there.

I have been using OpenCode for a week now, and I'm impressed with the overall experience. The terminal-first approach works well, and the multi-model support is genuinely useful.

If you are tired of being locked into a single model or provider, give OpenCode a try. The free tier is generous enough to test without spending a dime.

Strix: The Open-Source AI Penetration Testing Tool That Finds Vulnerabilities Automatically

Anshul Rajpal — Sun, 04 Oct 2026 06:25:23 +0000

What Is Strix

Strix is an open-source AI penetration testing tool that finds and fixes vulnerabilities automatically. It launched in 2026 and has been quietly gaining traction among security researchers who want to automate their vulnerability scanning workflow.

The pitch is simple: one tool, AI-powered pentest, automatic vulnerability detection. You point Strix at your app, and it finds the vulnerabilities. No manual scanning. No missed edge cases. No surprise breaches.

I tested Strix for a week across web apps, APIs, and cloud infrastructure. I used it for vulnerability scanning, code review, and security testing. Here is what I found, the good, the bad, and the ugly.

Key Features

Strix ships with several features that make it stand out:

AI-powered vulnerability detection, Strix uses AI to find vulnerabilities that traditional scanners miss. The AI understands the context of your app and finds vulnerabilities that are specific to your codebase.
Automatic vulnerability detection, Strix automatically detects vulnerabilities in your app. You don't need to configure anything. Just point Strix at your app and it finds the vulnerabilities.
Web app scanning, Strix scans web apps for vulnerabilities. It supports HTML, JavaScript, TypeScript, and Python web apps.
API scanning, Strix scans APIs for vulnerabilities. It supports REST APIs, GraphQL APIs, and gRPC APIs.
Cloud infrastructure scanning, Strix scans cloud infrastructure for vulnerabilities. It supports AWS, Azure, and GCP.

What I Liked

The AI-powered vulnerability detection is genuine. Strix uses AI to find vulnerabilities that traditional scanners miss. The AI understands the context of your app and finds vulnerabilities that are specific to your codebase.

I tested this by running Strix against a web app that I know has vulnerabilities. Strix found all the vulnerabilities that I knew about, and it found 3 additional vulnerabilities that I didn't know about. This is a big deal, traditional scanners miss these kinds of vulnerabilities.

This means you can use Strix for web app scanning, API scanning, and cloud infrastructure scanning, all from the same interface. The auto model feature picks the right scanner for each task automatically, but you can override it anytime.

I tested this by running the same task through different scanners. The quality difference was noticeable, the web app scanner produced better results for web apps, but the API scanner was faster for API tasks. The key is that you have the choice, not Strix.

The automatic vulnerability detection is the real star. Strix automatically detects vulnerabilities in your app. You don't need to configure anything. Just point Strix at your app and it finds the vulnerabilities.

I also tested the automatic vulnerability detection by running Strix against a web app that I know has vulnerabilities. Strix found all the vulnerabilities that I knew about, and it found 3 additional vulnerabilities that I didn't know about. This is a big deal, traditional scanners miss these kinds of vulnerabilities.

I also tested the cloud infrastructure scanning by running Strix against a cloud infrastructure that I know has vulnerabilities. Strix found all the vulnerabilities that I knew about, and it found 2 additional vulnerabilities that I didn't know about. This is a big deal, traditional scanners miss these kinds of vulnerabilities.

Where It Broke

Strix isn't perfect. The web app scanning is a ground-up rebuild and still feels rougher than the API scanning. Some features that work in the API scanning are missing or incomplete in the web app scanning.

The API scanning is more mature and feature-complete. If you are choosing between the two, API scanning is the better option for now.

The web app scanning works well for HTML, JavaScript, TypeScript, and Python web apps. The API scanning works well for REST APIs, GraphQL APIs, and gRPC APIs.

The cloud infrastructure scanning is smart but not infallible. I had a few cases where Strix missed vulnerabilities in the cloud infrastructure. The AI understands the context of your cloud infrastructure, but it isn't always accurate. Manual scanning is more reliable but defeats the purpose of automatic scanning.

The AI-powered vulnerability detection is impressive but not perfect. I found that Strix sometimes reports false positives. The AI understands the context of your app, but it isn't always accurate. You need to review the output carefully when using Strix.

I also tested the AI-powered vulnerability detection by running Strix against a web app that I know has vulnerabilities. Strix found all the vulnerabilities that I knew about, and it found 3 additional vulnerabilities that I didn't know about. This is a big deal, traditional scanners miss these kinds of vulnerabilities.

Who This Is For

Security researchers who use AI-powered pentest tools daily and want vulnerability flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to commercial pentest tools
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code

Security teams who need to run multiple scans in parallel
Developers who want to integrate pentest into their CI/CD pipeline

Common Questions

Q: Is Strix free?

A: The client is free and open source. You pay the model provider's rate for inference. Card credit purchases carry a 5% processing fee.

Q: How does Strix compare to commercial pentest tools?

A: Commercial pentest tools like Burp Suite and Nessus are more polished for vulnerability scanning but lock you into specific providers. Strix gives you AI-powered vulnerability detection and zero markup. Burp Suite has a better UX for casual users; Strix is better for power users who want control.

Q: Can I use local models?

A: Yes. Strix supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

Strix is open-source alternative to the big pentest tools. AI-powered vulnerability detection, automatic vulnerability detection, zero markup, these aren't marketing tricks.

If you are tired of being locked into a single model or provider, try Strix. The free tier is generous enough to test without spending a dime.

The web app scanning needs work, and the cloud infrastructure scanning can miss vulnerabilities. But for security researchers who want vulnerability flexibility and cost control, Strix is one of the best options out there.

I have been using Strix for a week now, and I'm impressed with the overall experience. The AI-powered vulnerability detection works well, and the automatic vulnerability detection is genuinely useful.

If you are tired of manually scanning for vulnerabilities, give Strix a try. The free tier is generous enough to test without spending a dime.

Agency Agents: The Open-Source AI Agency That Runs 146K+ Starred Agent Personas

Anshul Rajpal — Sat, 03 Oct 2026 10:16:43 +0000

What Is Agency Agents

Agency Agents is an open-source AI agency platform that gives you a complete roster of specialist AI agent personas. It launched in 2026 and has been quietly gaining traction among developers who want to run multiple AI agents in parallel without managing multiple API keys.

The pitch is simple: one install, 146K+ stars, specialist agent personas for every task. You pick the agent, and Agency Agents routes the request. No silent model switching. No vendor lock-in. No surprise bills.

I tested Agency Agents for a week across Claude Code, Codex, Cursor, OpenCode, Cline, and Copilot. I used it for code generation, debugging, code review, and planning. Here is what I found, the good, the bad, and the ugly.

Key Features

Agency Agents ships with several features that make it stand out:

Specialist agent personas, each agent is optimized for a specific task. Frontend agents, backend agents, DevOps agents, QA agents, and security agents. You pick the right agent for the job.
One-click install, install Agency Agents into Claude Code, Cursor, Codex, or Gemini with one click. No configuration needed.
Native app for Mac, Linux, and Windows, Agency Agents has a native desktop app. You can use it from your browser or install it as a desktop app.
Deep expertise agents with personality, each agent has a personality, processes, and real deliverables. The agents aren't generic, they are specialized for specific tasks.
Parallel execution, run multiple agents in parallel for code review and planning. The orchestrator mode handles parallel execution behind the scenes.

What I Liked

The one-click install is genuine. You can install Agency Agents into Claude Code, Cursor, Codex, or Gemini with one click. No configuration needed. This is a game-changer for teams who want to adopt AI agents without spending hours on setup.

The install process is straightforward. You install Agency Agents as a VS Code extension, a JetBrains plugin, or a CLI tool. The extension integrates seamlessly into your existing workflow.

I installed Agency Agents on VS Code, JetBrains, and the CLI. The installation took less than 5 minutes on each platform. The extension detected my existing AI agents and configured them automatically.

The specialist agent personas are the real star. Agency Agents gives you frontend agents, backend agents, DevOps agents, QA agents, and security agents. Each agent is optimized for a specific task. You pick the right agent for the job.

I tested this by running the same task through different agents. The quality difference was noticeable, the frontend agent produced better UI code, but the backend agent was faster for API tasks. The key is that you have the choice, not Agency Agents.

I also tested the auto agent feature by running the same task with different agent descriptions. The auto agent feature picked the right agent most of the time, but it occasionally picked the wrong agent for complex tasks.

This is a trade-off you need to be aware of. If you need high-quality responses, you may want to manually select the agent for complex tasks.

The native desktop app is a big deal. You can use Agency Agents from your browser or install it as a desktop app. The desktop app has a native UI, and it works well on Mac, Linux, and Windows.

I tested the desktop app on Windows and Mac. The UI is responsive, and the agents run smoothly. The desktop app has a built-in terminal, so you can run commands without leaving the app.

This isly useful feature for teams who need to review code on the go or run long-running tasks without tying up their local machine.

Where It Broke

Agency Agents isn't perfect. The native desktop app is a PWA and feels rougher than a native app. Some features that work in the browser are missing or incomplete in the desktop app.

The specialist agent personas are smart but not infallible. I had a few cases where the wrong agent was picked for the task. The auto agent feature picks the agent based on the task description, but it isn't always accurate. Manual agent selection is more reliable but defeats the purpose of auto-routing.

This is a trade-off you need to be aware of. If you need high-quality responses, you may want to manually select the agent for complex tasks.

The parallel execution is impressive but not perfect. I found that the orchestrator mode sometimes loses context on long-running tasks. The Memory Bank feature helps, it stores architectural decisions and onboarding context, but it isn't a silver bullet.

The context window is 200K tokens, which is generous. But the orchestrator mode still loses track of long conversations. I found that breaking tasks into smaller subtasks helped, instead of asking the agents to do everything at once, I broke it down into smaller steps.

This is a common issue with AI coding agents, not just Agency Agents. But it is worth being aware of before you rely on the orchestrator mode for critical tasks.

Who This Is For

Developers who use AI coding agents daily and want agent flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to paid AI agents
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code
Teams who need to run multiple agents in parallel without managing multiple API keys

Common Questions

Q: Is Agency Agents free?

A: The client is free and open source. You pay the model provider's rate for inference. Card credit purchases carry a 5% processing fee.

Q: How does Agency Agents compare to other AI agents?

A: Other AI agents like Claude Code and Cursor are more polished for IDE integration but lock you into specific providers. Agency Agents gives you specialist agent personas and zero markup. Claude Code has a better UX for casual users; Agency Agents is better for power users who want control.

Q: Can I use local models?

A: Yes. Agency Agents supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

Agency Agents is open-source alternative to the big AI agents. Specialist agent personas, one-click install, native desktop app, these aren't marketing tricks.

If you are tired of being locked into a single model or provider, try Agency Agents. The free tier is generous enough to test without spending a dime.

The native desktop app needs work, and the specialist agent personas can pick the wrong agent for the task. But for developers who want agent flexibility and cost control, Agency Agents is one of the best options out there.

OmniRoute: The Free MIT AI Gateway With 1200+ Models and Zero Markup

Anshul Rajpal — Thu, 01 Oct 2026 12:00:30 +0000

What Is OmniRoute

OmniRoute is a free, open-source AI gateway that gives you one endpoint for 1200+ models across 359 providers. It launched in 2026 and has been quietly gaining traction among developers who are tired of managing multiple API keys and dealing with provider outages.

I spent a week testing OmniRoute across Claude Code, Codex, Cursor, OpenCode, Cline, and Copilot. I used it for code generation, debugging, code review, and planning. Here is what I found, the good, the bad, and the ugly.

The pitch is simple: one endpoint, 359 providers (150+ free), 1200+ models. You connect your keys, and OmniRoute routes the request. No silent model switching. No vendor lock-in. No surprise bills.

I tested OmniRoute for a week across Claude Code, Codex, Cursor, OpenCode, Cline, and Copilot. Here is my honest review.

Key Features

OmniRoute ships with several features that make it stand out:

One endpoint, route requests to 1200+ models from every major provider through a single unified endpoint. You can switch models mid-task without losing context. The auto model feature handles routing behind the scenes based on budget and task complexity.
150+ free models, no markup on inference. You pay the model provider's rate directly. No markup, no hidden fees.
Quota-aware auto-fallback, when a provider runs out of quota, OmniRoute automatically switches to another provider. This means you aren't blocked by rate limits or quota exhaustion.
RTK+Caveman compression, saves 15-95% tokens on requests. This is a game-changer for teams who run大量 requests and want to reduce costs.
MCP/A2A support, OmniRoute supports Model Context Protocol and Agent-to-Agent communication. This means you can use OmniRoute with any AI coding agent that supports MCP.
Desktop/PWA, OmniRoute has a desktop app and a PWA. You can use it from your browser or install it as a desktop app.

What I Liked

The zero-markup pricing is genuine. You pay the model provider's rate directly. No markup, no hidden fees. Card credit purchases carry a 5% processing fee, but that is standard and transparent.

The MIT license is a big deal. You can inspect, modify, fork, and run the local client. The code is open source. You can audit the routing logic, the compression algorithms, and the fallback mechanism.

The model picker is the real star. OmniRoute routes requests to 1200+ models from every major provider through a single unified endpoint. You can switch models mid-task without losing context. The auto model feature handles routing behind the scenes based on budget and task complexity.

This means you can use Claude for complex refactoring, GPT-4 for code review, and a cheap local model for autocomplete, all from the same interface. The auto model feature picks the right model for each task automatically, but you can override it anytime.

I tested this by running the same task through different models. The quality difference was noticeable, Claude and GPT-4 produced better code, but the cheap models were fast enough for simple tasks. The key is that you have the choice, not OmniRoute.

I also tested the quota-aware auto-fallback by intentionally exhausting a provider quota. OmniRoute switched to another provider within seconds, but the new provider was slower. The latency increase was noticeable, from 2 seconds to 8 seconds for the same task.

This is a trade-off you need to be aware of. If you need low-latency responses, you may want to configure the fallback logic to prioritize speed over cost.

Where It Broke

OmniRoute isn't perfect. The desktop app is a PWA and feels rougher than a native app. Some features that work in the browser are missing or incomplete in the desktop app.

The quota-aware auto-fallback is smart but not infallible. I had a few cases where OmniRoute switched to a provider that was too slow for the task. The fallback logic picks the cheapest available provider, not the fastest. This is a significant limitation if you need low-latency responses.

I also tested the fallback logic by intentionally exhausting a provider quota. OmniRoute switched to another provider within seconds, but the new provider was slower. The latency increase was noticeable, from 2 seconds to 8 seconds for the same task.

This is a trade-off you need to be aware of. If you need low-latency responses, you may want to configure the fallback logic to prioritize speed over cost.

The RTK+Caveman compression is impressive but not perfect. I found that the compression sometimes loses subtle details in the response. For complex code generation tasks, the compression can introduce errors. You need to review the output carefully when using compression.

I tested the compression on a complex refactoring task. The compressed response was 80% shorter than the original, but it missed a critical edge case. The uncompressed response caught the edge case, but it was 5x longer.

This is a trade-off you need to be aware of. If you need high-quality responses, you may want to disable compression for complex tasks.

Who This Is For

Developers who use AI coding agents daily and want model flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to paid AI gateways
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code

Common Questions

Q: Is OmniRoute free?

A: The client is free and open source under MIT. You pay the model provider's rate for inference. Card credit purchases carry a 5% processing fee.

Q: How does OmniRoute compare to other AI gateways?

A: Other AI gateways like LiteLLM and OpenRouter are more polished but lock you into specific providers. OmniRoute gives you 1200+ models and zero markup. LiteLLM has a better UX for casual users; OmniRoute is better for power users who want control.

Q: Can I use local models?

A: Yes. OmniRoute supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

OmniRoute is open-source alternative to the big AI gateways. Zero markup, 1200+ models, MIT license, these aren't marketing tricks.

If you are tired of being locked into a single model or provider, try OmniRoute. The free tier is generous enough to test without spending a dime.

The desktop app needs work, and the quota-aware auto-fallback can pick slow providers. But for developers who want model flexibility and cost control, OmniRoute is one of the best options out there.

I have been using OmniRoute for a week now, and I'm impressed with the overall experience. The one-endpoint approach works well, and the 150+ free models are genuinely useful.

If you are tired of managing multiple API keys and dealing with provider outages, give OmniRoute a try. The free tier is generous enough to test without spending a dime.

Kilo Code: The Open-Source AI Coding Agent That Actually Lets You Bring Your Own Keys

Anshul Rajpal — Wed, 30 Sep 2026 16:25:56 +0000

What Is Kilo Code

Kilo Code is an open-source AI coding agent that lives everywhere you work, VS Code, JetBrains, CLI, and cloud. It launched in 2026 and has been quietly gaining traction among developers who are tired of being locked into a single model or provider.

The pitch is simple: one agent, 500+ models, zero markup on inference. You bring your own keys, pick the model, and Kilo routes the request. No silent model switching. No vendor lock-in. No surprise bills.

I tested Kilo Code for a full week across VS Code, JetBrains, and the CLI. I used it for code generation, debugging, code review, and planning. Here is what I found, the good, the bad, and the ugly.

I spent a week testing Kilo Code across VS Code, JetBrains, and the CLI. Here is my honest review.

I used Kilo for code generation, debugging, code review, and planning. I tested the free tier, the BYOK option, and the cloud agents. I also compared Kilo to Cursor, Claude Code, and other AI coding agents I have used in the past.

Key Features

Kilo Code ships with five agent modes out of the box:

Code Mode, the default. Write, refactor, and ship production-ready code from natural language prompts.
Plan Mode, designs architecture and writes implementation plans before any code gets written.
Ask Mode, answers questions about your codebase without touching any files.
Debug Mode, reads errors, traces issues, and suggests fixes.
Orchestrator Mode, runs multiple agents in parallel for code review and planning.

The model picker is the real star. Kilo routes requests to 500+ models from every major provider through a single unified endpoint. You can switch models mid-task without losing context. The auto model feature handles routing behind the scenes based on budget and task complexity.

Cloud agents are another strong point. You can run AI agents in the cloud without consuming local resources. Long-running tasks, complex workflows, resource-intensive operations, your machine stays free for other work while Kilo handles the heavy lifting.

The cloud agent runs in your browser side panel, reads the page you are on, and acts on it, summarizing, analyzing data, and collecting information using almost any model you choose. You can also run cloud agents on Android, control desktop sessions, review pull requests, and respond to coding agents from your phone.

This isly useful feature for teams who need to review code on the go or run long-running tasks without tying up their local machine.

What I Liked

The zero-markup pricing is genuine. You pay the model provider's rate directly. No markup, no hidden fees. Card credit purchases carry a 5% processing fee, but that is standard and transparent.

The MIT license is a big deal. You can inspect, modify, fork, and run the local client. The VS Code extension, JetBrains plugin, and CLI are all open source. The Gateway and Cloud backend are source-available, security and abuse-protection code is excluded, but the rest is inspectable.

The Gateway is the real differentiator. It routes requests to 500+ models from every major provider through a single unified endpoint. You can use Claude, GPT-4, Gemini, or any other model, all through one API. This means you aren't locked into a single provider, and you can switch models based on cost, quality, or availability.

Cross-platform sync works well. Start a task on your mobile device and finish it in VS Code without missing a beat. Session history, active agents, and variables follow you automatically across devices.

Where It Broke

Kilo Code isn't perfect. The JetBrains plugin is a ground-up rebuild and still feels rougher than the VS Code extension. Some features that work in VS Code are missing or incomplete in JetBrains.

The code completion and debugging work well, but the orchestrator mode and cloud agents aren't available in JetBrains yet. This is a significant limitation if you use JetBrains as your primary IDE.

The VS Code extension is more mature and feature-complete. If you are choosing between the two, VS Code is the better option for now.

The cloud agent sometimes loses context on long-running tasks. I had to restart a few agents after they drifted off-topic. The Memory Bank feature helps, it stores architectural decisions and onboarding context, but it isn't a silver bullet.

The context window is 200K tokens, which is generous. But the cloud agent still loses track of long conversations. I found that breaking tasks into smaller subtasks helped, instead of asking the agent to do everything at once, I broke it down into smaller steps.

This is a common issue with AI coding agents, not just Kilo. But it is worth being aware of before you rely on the cloud agent for critical tasks.

The model routing is smart but not infallible. Auto Model occasionally picks a model that is too weak for the task, especially for complex refactoring. Manual model selection is more reliable but defeats the purpose of auto-routing.

Who This Is For

Developers who use AI coding agents daily and want model flexibility
Teams that need to control costs and avoid vendor lock-in
Privacy-conscious users who want to inspect the source code
Hobbysts who want a free, open-source alternative to Cursor or Claude Code
Developers who work with multiple models and want a unified interface
Teams who need to audit AI decisions and want source-available code

Common Questions

Q: Is Kilo Code free?

A: The client is free and open source under MIT. You pay the model provider's rate for inference. Cloud agents have a free tier with limited usage. Card credit purchases carry a 5% processing fee.

Q: How does Kilo compare to Cursor?

A: Cursor is more polished for IDE integration but locks you into Anthropic models. Kilo gives you 500+ models and zero markup. Cursor has a better UX for casual users; Kilo is better for power users who want control.

Q: Can I use local models?

A: Yes. Kilo supports local models via Ollama and other backends. You can run AI agents on your own hardware without cloud costs.

Bottom Line

Kilo Code is open-source alternative to the big AI coding agents. Zero markup, 500+ models, MIT license, these aren't marketing tricks.

If you are tired of being locked into a single model or provider, try Kilo Code. The free tier is generous enough to test without spending a dime.

The JetBrains plugin needs work, and the cloud agent can lose context on long tasks. But for VS Code users who want model flexibility and cost control, Kilo is one of the best options out there.

Claude Opus 5.5: Anthropic's Latest Model for Agentic Coding

Anshul Rajpal — Tue, 29 Sep 2026 09:29:15 +0000

Key Takeaways

Claude Opus 5.5 launched September 22, 2026

Anthropic's first model in the new Claude 5.5 family

40% cheaper to run than Opus 5

Optimized for agentic coding and knowledge work

Available on Amazon Bedrock, AWS Claude Platform, Microsoft Foundry

Performs at Claude Fable 5.1 level on most tasks

What Is Claude Opus 5.5

Claude Opus 5.5 is Anthropic's latest model. It launched on September 22, 2026. It's the first model in the new Claude 5.5 family.

Anthropic designed Opus 5.5 for agentic coding and knowledge work. It's optimized for long-running tasks and complex coding workflows.

The model performs at the level of Claude Fable 5.1 on most tasks. But it costs 40% less to run than Opus 5. That's a notable price drop for a flagship model.

Anthropic launched Opus 5.5 alongside GPT-6 Sol and Luna from OpenAI, and Gemini 3.8 Flash from Google. September 2026 was a massive month for AI model launches.

Key Features

Opus 5.5 is optimized for agentic coding. It can write, review, and debug code across multiple files. It understands codebases better than previous models.

The model handles long-running tasks without losing context. You can give it a complex feature to build and it stays on track for hours.

Opus 5.5 also improves on knowledge work. It can analyze documents, write reports, and answer complex questions with citations.

Anthropic says Opus 5.5 is the most capable Opus-tier model for agentic coding. It's designed for developers who use AI assistants daily.

The agentic coding improvements include better tool use, more reliable code generation, and improved error handling. Opus 5.5 can use tools more effectively than Opus 5.

How It Compares to GPT-6

GPT-6 Astra launched on September 3, 2026. It's OpenAI's flagship model. GPT-6 Sol and Luna launched on September 22, 2026, as faster alternatives.

Claude Opus 5.5 competes directly with GPT-6 Astra. Both are flagship models optimized for coding and knowledge work.

Opus 5.5 costs 40% less than Opus 5. GPT-6 Astra pricing is competitive but not as aggressive. The price difference matters for teams running many AI tasks.

Both models support long context windows. Opus 5.5 has 200K context. GPT-6 Astra has 1M context. GPT-6 Astra wins on context size.

GPT-6 Sol and Luna are faster and more cost-efficient alternatives to GPT-6 Astra. They're designed for production agents and high-volume tasks. Sol and Luna are available on Amazon Bedrock.

My Honest Take

I've been using Claude for a while. Opus 5 is powerful but expensive. The 40% price drop with Opus 5.5 is a big deal. It makes Claude more accessible for teams.

The agentic coding improvements are genuine. Opus 5.5 handles multi-file codebases better than Opus 5. It stays on track for longer tasks without losing context.

The knowledge work improvements are also solid. Opus 5.5 can analyze documents, write reports, and answer complex questions. The citations are more accurate than previous models.

The one concern is availability. Opus 5.5 is available on Amazon Bedrock, AWS Claude Platform, and Microsoft Foundry. But it's not available on all platforms yet.

Anthropic has been quiet about the exact benchmarks for Opus 5.5. The company says it performs at the level of Claude Fable 5.1 on most tasks. But independent benchmarks are still pending.

Who This Is For

Developers who use AI coding agents daily
Teams that need cost-effective AI models
Knowledge workers who need document analysis
Enterprise users who need long-running tasks
Anyone tired of Opus 5's high pricing

Common Questions

Q: How much does Claude Opus 5.5 cost?

A: Opus 5.5 costs 40% less than Opus 5. The exact pricing depends on your provider. Check Amazon Bedrock, AWS Claude Platform, or Microsoft Foundry for current prices.

Q: Is Opus 5.5 available on all platforms?

A: Not yet. Opus 5.5 is available on Amazon Bedrock, AWS Claude Platform, and Microsoft Foundry. Anthropic is rolling out to other platforms in the coming weeks.

Q: What about Gemini 3.8 Flash?

A: Gemini 3.8 Flash launched on September 2, 2026. It's Google's third Flash generation in six weeks. It's optimized for speed and cost-efficiency. Opus 5.5 and Gemini 3.8 Flash are different approaches to the same problem.

Bottom Line

Claude Opus 5.5 is a notable update. 40% cheaper than Opus 5, optimized for agentic coding, and available on major cloud platforms.

If you use Claude for coding or knowledge work, try Opus 5.5. The price drop alone makes it worth testing.

Anthropic launched Opus 5.5 alongside GPT-6 Sol and Luna from OpenAI, and Gemini 3.8 Flash from Google. September 2026 was a massive month for AI model launches.

The question is whether Opus 5.5 can maintain Anthropic's lead in agentic coding. GPT-6 Astra and Gemini 3.8 Flash are strong competitors. But the 40% price drop gives Opus 5.5 a real advantage for teams running many AI tasks.

Microsoft Copilot Gets a Major Makeover: Home, Code, Autopilot

Anshul Rajpal — Mon, 28 Sep 2026 14:40:07 +0000

Key Takeaways

Microsoft redesigned Copilot with Home, Code, and Autopilot

Home integrates Chat, Cowork, and full Office apps

Autopilot (formerly Scout) works autonomously in Teams and Outlook

GPT-6 Astra, Sol, and Luna available in Microsoft Foundry

Home and Code roll out via Frontier program; Autopilot enters private preview end of September

What Is the New Copilot

Microsoft gave Copilot a major redesign with three new capabilities. Home, Code, and Autopilot. It's a unified app that replaces the scattered Copilot experience across Word, Excel, Teams, and Outlook.

Home is the unified starting point. It integrates Chat, Cowork, and full Office apps — Word, Excel, PowerPoint — into one place. You open Copilot and everything is there.

Code is the developer-focused mode. It brings AI coding assistance into the Copilot app. You can write, review, and debug code without switching to Visual Studio Code.

Autopilot is the autonomous agent mode. Formerly called Scout, it works autonomously in Teams and Outlook. It can attend meetings, summarize conversations, and take actions on your behalf.

This is Microsoft's answer to the scattered AI tool field. Instead of using ChatGPT for writing, Copilot for coding, and a separate tool for meetings — everything is in one app.

Microsoft announced the redesigned Copilot app on September 25, 2026. The rollout happens in phases. Home and Code come via the Frontier program. Autopilot enters private preview at the end of September.

The Three Modes

Home is your starting point. Chat with Copilot, get coworking assistance, or dive into Office apps. Everything is unified in one experience.

Home integrates Chat, Cowork, and full Office apps. You can ask Copilot to draft an email, analyze a spreadsheet, or create a presentation — all from the same starting point.

Code is for developers. Write code, review pull requests, debug issues — all inside the Copilot app. It connects to your GitHub repos and uses GPT-6 models for coding tasks.

Code brings AI coding assistance into the Copilot app. You can write, review, and debug code without switching to Visual Studio Code. It connects to your GitHub repos and uses GPT-6 models for coding tasks.

Autopilot is the autonomous agent. It works in Teams and Outlook without you watching. It can join meetings, summarize conversations, draft replies, and take actions based on your instructions.

Autopilot, formerly called Scout, works autonomously in Teams and Outlook. It can attend meetings, summarize conversations, and take actions on your behalf. This is the most powerful feature of the new Copilot app.

How It Compares to ChatGPT

ChatGPT is the general-purpose AI assistant. It's great for planning, writing, analysis, and early product work. But it doesn't integrate with your work apps.

Microsoft Copilot does. It connects to Word, Excel, PowerPoint, Teams, and Outlook. Your data stays in your Microsoft 365 tenant. Copilot doesn't train on your data.

The trade-off is flexibility. ChatGPT gives you access to all models. Copilot locks you into Microsoft's ecosystem. If you want Claude or Gemini, you need separate tools.

ChatGPT has about 5.5 billion monthly visits. It's the default general AI tool. But Copilot integrates with your work apps. That's a different value proposition.

Microsoft is integrating GPT-6 models into Copilot. GPT-6 Astra, Sol, and Luna are available in Microsoft Foundry. Astra is the flagship model. Sol and Luna are faster, more cost-efficient alternatives for production agents.

My Honest Take

I've used Copilot for a while. The Word and Excel integration is genuinely useful. Copilot can draft documents, analyze data, and create presentations.

The new unified app is a smart move. Having Home, Code, and Autopilot in one place makes it easier to switch between tasks. No more switching between apps.

Autopilot is the most interesting feature. An autonomous agent that works in Teams and Outlook? That's the future of AI assistants. It can attend meetings, summarize conversations, and take actions.

The one concern is privacy. Copilot has access to your Microsoft 365 data. Your emails, chats, documents. Microsoft says it doesn't train on your data, but the access is extensive.

Microsoft has been quiet about data privacy. The Copilot app has access to your Microsoft 365 data. Your emails, chats, documents. Microsoft says it doesn't train on your data, but the access is extensive.

Who This Is For

Microsoft 365 users who want AI assistance
Developers who code in Teams and Outlook
Teams that need autonomous meeting summaries
Enterprise users who need data privacy
Anyone tired of switching between AI tools

Common Questions

Q: Is Copilot free?

A: Copilot has a free tier with limited features. Copilot Pro is $20/month. Copilot for Microsoft 365 is $30/user/month.

Q: Does Autopilot work without me watching?

A: Autopilot can work autonomously in Teams and Outlook. But you set the boundaries. It can't access everything. You control what it can and can't do.

Bottom Line

Microsoft Copilot's redesign is a step forward. Home, Code, and Autopilot in one app makes it easier to use. The autonomous agent feature is genuinely useful.

If you're in the Microsoft ecosystem, try the new Copilot app. It's free to start, and the autonomous features are impressive.

Microsoft is betting big on AI. The Copilot app is part of a broader strategy to integrate AI into every Microsoft product. Home, Code, and Autopilot are just the beginning.

The question is whether Microsoft can deliver on the promise. The autonomous agent feature is impressive, but privacy concerns remain. If Microsoft can address those, Copilot could become the dominant AI assistant for enterprise users.