Hey everyone! 👋

Just found a solid offer:

Kiro Pro + Claude 4.7 completely free for 1 month.

Here's the step-by-step (tested and working):

*Go to → https://app.kiro.dev/signin

Sign in with Google

*Choose KIRO PRO (it will show $20 at first but after selection you will see $0)

*Add your payment method and complete the process (total stays $0)

*Once activated, go immediately to Billing Settings → switch your subscription to Free

You keep all Pro features (including Claude 4.7) for the full month

No charge at the end of the month if you cancel.

Perfect way to try Claude 4.7 Pro for free.

Let me know in the comments if it worked for you too!

Hey, I'm fed up with accepting all the terminal commands.
In settings I have:
-global: auto approve (experimental) turned on
-agent autonomy - autopilot set

Still this has no effect on that. Am I missing something?

This should be an option, they are open weights with friendly licenses. Also, Deepseek v4, GLM-5.1, and one variant of Qwen3.6.

Hi, I just came across to Kiro and am I the only one who thinks that Kiro seems to be underrated? The pricing seems to be really fair (yet). 1000 Credits per month when I pay 20$ and Opus 4.7 uses just 2.2 Credits, that means that I have 454 prompts per month with just Opus 4.7 ?!

Isn't it better than Cursor, Copilot and Claude Code in terms of pricing? Only disadvantage I faced is that you are not that flexible as for example Cursor regarding different AI models.

Kiro is dumb today again. Holly fck...

Bugged and super dumb

Seriously? Just use it on auto and not on the top Claude models, and 500 credits will last you a week.

Then you can just create a new acc with a VPN and ull get 500 again. And keep all of your chats in the IDE.

I mean I get why you would use opus bc everybody and their dog is going crazy about it, but it's just not needed.

And I saved a lot of credits by just explaining my Idea to gemini and letting it correct the specs.mds and write the prompts.

My friend signed up for an account, but they didn't give him the 500 bonus credits

Coding agents are powerful ships, but they’re sailing without a map. They can write code, run tests, and iterate — but they don’t know where they are in the codebase. Context engineering is the discipline of giving agents the architectural awareness they need to navigate effectively. Without it, even the best models waste tokens exploring dead ends. With it, a cheap model outperforms an expensive one.

https://medium.com/@xanther.ai/context-engineering-is-the-compass-your-coding-agent-needs-6eef30c66286?postPublishedType=initial

The Navigation Problem

Picture a ship in open water. It has a powerful engine, a skilled crew, and enough fuel to reach any destination. But it has no compass, no charts, and no GPS. What happens?

It explores. It tries directions. It backtracks when it hits land where it expected open water. Eventually, through trial and error, it might reach its destination — but it burns 3x the fuel and takes 5x the time.

This is exactly what happens when you point a coding agent at a large codebase without architectural context.

https://preview.redd.it/nr5idnhzj90h1.png?width=720&format=png&auto=webp&s=90ca6ff90066501de6e3f0c66828309d212b2832

The agent has all the capabilities it needs. It can read files, write code, run tests, search for patterns. But it doesn’t know the architecture. It doesn’t know that django/db/models/sql/compiler.py is the heart of query generation, or that changing BaseCache.set() affects every cache backend downstream. It discovers these things through exploration — expensive, token-heavy, error-prone exploration.

Without context engineering:

Agent: "I need to fix the cache race condition"
→ Searches for "cache" → finds 47 files
→ Reads django/core/cache/__init__.py → not helpful
→ Reads django/core/cache/backends/filebased.py → finds the class
→ Reads django/core/cache/backends/base.py → understands inheritance
→ Searches for "thread" → finds 23 files
→ Reads django/utils/autoreload.py → wrong file
→ Reads django/core/files/locks.py → relevant but doesn't know why yet
→ Eventually pieces together the architecture after 12 file reads
Total: ~4,000 tokens, 45 seconds, 2 wrong attempts

With context engineering:

Agent: "I need to fix the cache race condition"
→ Queries XCE: "FileBasedCache race condition threading"
→ Gets back: inheritance chain, threading concerns, related utilities, test infrastructure
→ Goes directly to the right files with full architectural understanding
Total: ~1,500 tokens, 15 seconds, correct on first attempt

Same agent. Same model. Same capabilities. The only difference is the map.

The Three Levels of Context

Not all context is created equal. There’s a hierarchy:

Level 1: Code Context (What exists)

This is what most tools provide today — file contents, function signatures, grep results. It answers “what code is here?” but not “why?” or “how does it connect?”

Tools at this level: file search, grep, symbol lookup, embeddings-based RAG.

Limitation: Finding a function doesn’t tell you what calls it, what it depends on, or what breaks if you change it.

Level 2: Structural Context (How things connect)

This captures relationships — call graphs, inheritance chains, import dependencies, module boundaries. It answers “what depends on what?” and “what’s the execution flow?”

Tools at this level: static analysis, dependency graphs, call chain extraction.

Limitation: Knowing the call graph doesn’t tell you the design intent or architectural role of each component.

Level 3: Architectural Context (Why things exist)

This captures design intent — why a module exists, what role it plays in the system, what design patterns it implements, what constraints it must satisfy. It answers “what is this component’s job?” and “what are the rules?”

Tools at this level: XCE’s PRAT-powered structured index.

This is the level that changes agent behavior. When an agent knows that CsrfViewMiddleware must run before CacheMiddleware (and why), it doesn't accidentally break that constraint. When it knows that BaseCache defines a contract that all backends must satisfy, it doesn't write a fix that violates that contract.

https://preview.redd.it/6xf1g7t2k90h1.png?width=720&format=png&auto=webp&s=bf6efe957fc9eb347c86c5ffa4d5f9f940d88a5a

Why embeddings fail for this:

Embedding-based code search finds textually similar code. But the questions agents actually need answered are structural:

• "What depends on this function?" — not a text similarity question
• "If I change this file, what breaks?" — requires call graph knowledge
• "What's the inheritance chain?" — structural, not textual
• "What module owns this logic?" — architectural, not lexical

Two functions can be textually similar but architecturally unrelated. Two functions can be textually different but tightly coupled through a call chain. Embeddings can't distinguish these cases.

The compass metaphor:

A compass doesn't tell you the answer. It tells you which direction to look. That's what architectural context does for agents — it doesn't write the fix, but it tells the agent:

• Which files are relevant (and which aren't)
• How those files relate to each other
• What constraints must be preserved
• What patterns to follow
• What will break if you get it wrong
• The agent still does the work. But it does the right work, in the right place, on the first try.

Real numbers:

We tested this on SWE-bench Verified (500 real bugs from Django, scikit-learn, sympy, matplotlib, pytest):

https://preview.redd.it/klbpkr2mk90h1.png?width=805&format=png&auto=webp&s=bbe7166f5ad2336455749f9ec2581c4326de4e6a

A $0.02/call model with the right context beats a $0.30/call model without it. The improvement scales with complexity:

• Simple codebases (flat architecture): +8%
• Medium codebases (some layering): +12%
• Complex codebases (deep dependencies): +17%

This makes intuitive sense. If your codebase is a 500-line Express app, the agent doesn't need a map. If it's Django with 4,000 files across 50 modules with deep inheritance chains and cross-cutting middleware — the map is everything.

What we built:

We built a context layer that indexes codebases into a structural map (not just embeddings) and serves it via MCP. Any MCP-compatible agent (Claude Code, Cursor, Kiro, OpenCode, Windsurf, Cline) gets architectural context on every tool call without any changes to the agent itself.

npx xanther-cli init --api-key YOUR_KEY

One command indexes your repo. Then add to your agent's MCP config:

{
  "mcpServers": {
    "xanther-xce": {
      "url": "https://mcp.xanther.ai/sse?repo_id=YOUR_REPO_ID",
      "headers": { "Authorization": "Bearer YOUR_KEY" }
    }
  }
}

The agent gets five tools: xce_get_context (full architectural context for a problem), xce_search (semantic search), xce_architecture_context (deep dive on a file/symbol), xce_trace (trace code to architecture), xce_impact_analysis (what breaks if you change files).

The takeaway:

Everyone's focused on making models smarter. That matters. But the bottleneck for coding agents right now isn't model capability — it's context quality. A fast ship without a compass burns fuel going in circles. A slower ship with a compass reaches the destination first.

Context engineering — giving agents the right information at the right time — is the multiplier that makes every model better. And unlike model improvements (which require billions in training), context improvements are cheap and compound with every model upgrade.

Links:

• Full writeup: https://medium.com/@xanther.ai/context-engineering-is-the-compass-your-coding-agent-needs-6eef30c66286?postPublishedType=initial
• Website: https://xanther.ai
• Benchmark data (open): https://github.com/Xanther-Ai/xce-benchmarks
• npm: https://www.npmjs.com/package/xanther-cli
• Discord: https://discord.gg/YaBekKpR

Free tier: 3 repos, 100 queries/month. Curious what others think about this approach — is context the bottleneck you're hitting too?

Hey Guys,

If you've tried to find good Kiro sub-agents, you know the pain — they're scattered across dozens of GitHub repos with no central place to discover them.

So I built one: agents.kirorepository.online

It's a directory of curated sub-agents that I actually use day-to-day. Right now it has 12 agents across code review, security auditing, testing, DevOps, documentation, and more. Each one is a properly structured .md file — download it, drop it in ~/.kiro/agents/, and invoke it with /agent-name.

A few highlights:

• code-reviewer — structured reviews with Critical/Major/Minor severity tags
• security-auditor — OWASP Top 10, secrets scanning, dependency CVEs
• test-writer — generates tests for Jest, JUnit, pytest, Go
• pr-summarizer — writes your PR description from the git diff
• infra-reviewer — reviews Terraform, CloudFormation, and K8s manifests
• The whole thing was built with Kiro — specs, hooks, steering files, the works. Felt appropriate.

If you have agents you've built and use regularly, submit them to the site. The goal is to make this the go-to place for the Kiro agent ecosystem as it grows — right now the community is small enough that we can actually build something good here before it gets noisy.

Would love to hear what agents you're all using. Happy Kiro'ing! 👻

Sub-agents in Kiro: https://kiro.dev/docs/chat/subagents/

I paid for a subscription specifically to use Opus 4.7 and for the past two days it has been completely unusable. Every single attempt fails with the same error:

"The model you've selected is experiencing a high volume of traffic. Try changing the model and re-running your prompt."

Two full days. I have not been able to complete a single prompt. Not one.

Hey, recently after kiro got updated it is noticed that it only gives 50 credits. Any way I can still get 500 credits per login?

This has been happening way too often of late. Doesn't matter if I'm using Opus 4.7 or 4.6 (though switching to the other when it happens seems to help) -- I just hate how much it interrupts a flow. Set up a long running task to go, building out a spec and come back to find 3 minutes in it chucked that error. AGAIN.

Do better amazon, you're literally hosting the models!

Hello what is the default thinking of opus 4.6

And why we are still not seeing opus 4.7

And ChatGPT joined bedrock so when are we going to see ChatGPT 5.5

Please someone answer me!

Is anyone else using v0.12.155 seeing the "Start task" options disappear when you run a task and not come back after it is complete? I know that I could just type in the prompt and tell Kiro to run the next task, but having the clickable option is just easier.

So this seems to be the glitch that people have mentioned they have been banned over. They have a bug that randomly grants you 500 credits. According to others, they will then accuse you of gaming their system for free credits and ban you, not even allowing you to use the credits you paid for. Then they will charge you again next month even though you are still banned. In anticipation of that, I contacted the billing department to let them know that I did not ask for those credits, but in typical faceless corporation form, they haven't responded after several days.

If they ban me, it will be my personal mission to make it effortless to switch away from them and get the exact same features. (minus the deal they managed to secure on API usage with Anthropic.)