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How do you know if your AI app actually works? You evaluate it. But most teams skip this step (or do it wrong) because "eval" feels vague. It's not. Every good eval is a 3-step recipe. Step 1: Pick a task. AI systems have different capabilities and dimensions to evaluate. For LLMs, it can be safety or math capability, in RAGs it can be grounding and retrieval, Pick one. Step 2: Collect eval data. For every task, gather inputs paired with the right answer or expected behavior. A safety set pairs ...

ByteByteGo — Mon, 04 May 2026 16:23:38 GMT

How do you know if your AI app actually works?

You evaluate it. But most teams skip this step (or do it wrong) because "eval" feels vague. It's not.

Every good eval is a 3-step recipe.

Step 1: Pick a task. AI systems have different capabilities and dimensions to evaluate. For LLMs, it can be safety or math capability, in RAGs it can be grounding and retrieval, Pick one.

Step 2: Collect eval data. For every task, gather inputs paired with the right answer or expected behavior. A safety set pairs risky prompts with "refuse."

Step 3: Develop a grader. How do you decide if the output is good?

Use code-based graders (if/else, unit tests) for things with a clear correct answer and patch passing unit-tests.
Use model-based graders (LLM-as-judge) for subjective tasks like safety.
Use human graders for edge cases and anything where nuance matters more than throughput.

Most production evals combine all three. Code-based for what's cheap to check. Model-based for scale. Human-based for what matters most.

Over to you: what's the hardest thing about your task to grade, and which grader type do you use for it?

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CI/CD Pipeline Explained in Simple Terms Section 1 - SDLC with CI/CD The software development life cycle (SDLC) consists of several key stages: development, testing, deployment, and maintenance. CI/CD automates and integrates these stages to enable faster, more reliable releases. When code is pushed to a git repository, it triggers an automated build and test process. End-to-end (e2e) test cases are run to validate the code. If tests pass, the code can be automatically deployed to staging/produc...

ByteByteGo — Sun, 03 May 2026 09:23:38 GMT

CI/CD Pipeline Explained in Simple Terms

Section 1 - SDLC with CI/CD
The software development life cycle (SDLC) consists of several key stages: development, testing, deployment, and maintenance. CI/CD automates and integrates these stages to enable faster, more reliable releases.

When code is pushed to a git repository, it triggers an automated build and test process. End-to-end (e2e) test cases are run to validate the code. If tests pass, the code can be automatically deployed to staging/production.

Section 2 - Difference between CI and CD
Continuous Integration (CI) automates the build, test, and merge process. It runs tests whenever code is committed to detect integration issues early. This encourages frequent code commits and rapid feedback.

Continuous Delivery (CD) automates release processes like infrastructure changes and deployment. It ensures software can be released reliably at any time through automated workflows. CD may also automate the manual testing and approval steps required before production deployment.

Section 3 - CI/CD Pipeline
A typical CI/CD pipeline has several connected stages:
- Developer commits code changes to source control
- CI server detects changes and triggers build
- Code is compiled, tested (unit, integration tests)
- Test results reported to developer
- On success, artifacts are deployed to staging environments
- Further testing may be done on staging before release
- CD system deploys approved changes to production

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Last day to get free access to all 7 ByteByteGo courses for 1 month. Link at the end. - System Design Interview Vol. 1 - System Design Interview Vol. 2 - Machine Learning System Design Interview - Coding Interview Patterns - Object-Oriented Design Interview - Generative AI System Design Interview - Mobile System Design Interview Whether you’re preparing for interviews or looking to deepen your architecture knowledge, this is a great opportunity. Offer ends May 1. Check it out now at:

ByteByteGo — Fri, 01 May 2026 09:23:38 GMT

Last day to get free access to all 7 ByteByteGo courses for 1 month. Link at the end.

- System Design Interview Vol. 1
- System Design Interview Vol. 2
- Machine Learning System Design Interview
- Coding Interview Patterns
- Object-Oriented Design Interview
- Generative AI System Design Interview
- Mobile System Design Interview

Whether you’re preparing for interviews or looking to deepen your architecture knowledge, this is a great opportunity.

Offer ends May 1.

Check it out now at: https://bytebytego.com

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All 7 ByteByteGo courses are FREE to access and end in 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 3 𝐝𝐚𝐲𝐬. Link at the end. - System Design Interview Vol. 1 - System Design Interview Vol. 2 - Machine Learning System Design Interview - Coding Interview Patterns - Object-Oriented Design Interview - Generative AI System Design Interview - Mobile System Design Interview Whether you’re preparing for interviews or looking to deepen your architecture knowledge, this is a great opportunity. Offer end...

ByteByteGo — Wed, 29 Apr 2026 09:23:38 GMT

All 7 ByteByteGo courses are FREE to access and end in 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 3 𝐝𝐚𝐲𝐬. Link at the end.

- System Design Interview Vol. 1
- System Design Interview Vol. 2
- Machine Learning System Design Interview
- Coding Interview Patterns
- Object-Oriented Design Interview
- Generative AI System Design Interview
- Mobile System Design Interview

Whether you’re preparing for interviews or looking to deepen your architecture knowledge, this is a great opportunity.

Offer ends May 1.

Check it out now at: https://bytebytego.com/

#systemdesign #coding #interviewtips
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MCP vs Skills, Clearly Explained. Both MCP and Skills extend what an agent can do. But they solve different problems, and picking the wrong one adds cost or complexity you don't need. The diagram breaks down the five dimensions that matter. 1. Integration: MCP is a client-server protocol that connects N agents to M backends through one interface. Agent Skills are folders with a SKILL.md that the agent loads on trigger. 2. Architecture: MCP runs as a separate process with its own runtime, speakin...

ByteByteGo — Tue, 28 Apr 2026 09:23:38 GMT

MCP vs Skills, Clearly Explained.

Both MCP and Skills extend what an agent can do. But they solve different problems, and picking the wrong one adds cost or complexity you don't need.

The diagram breaks down the five dimensions that matter.

1. Integration: MCP is a client-server protocol that connects N agents to M backends through one interface. Agent Skills are folders with a SKILL.md that the agent loads on trigger.
2. Architecture: MCP runs as a separate process with its own runtime, speaking JSON-RPC. A Skill is just a directory: SKILL.md, optional scripts, references, and assets.
3. Invocation: MCP tools are called with typed parameters validated against a schema, and can be chained. Skills are invoked by the agent reading SKILL.md and running whatever commands it describes like bash, python, or curl.
4. Runtime: MCP servers often run in their own container or service. Skills run in the agent's own environment with no extra infra.
5. Where it fits: Use MCP to connect agents to live systems and data. Use Skills to give agents reusable know-how and instructions.

Over to you: What's the most interesting Skill you've come across recently?

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SLA vs SLO vs SLI These three terms are related, but they mean different things. Knowing the difference helps you define what to measure, aim for, and promise your customers. Here's how they actually connect: - SLI (Service Level Indicator): This is the metric you're measuring. For a login service, it could be the ratio of successful login requests to total valid requests. It tells you how your service is performing right now. - SLO (Service Level Objective): You take that SLI and define a targe...

ByteByteGo — Fri, 24 Apr 2026 09:23:38 GMT

SLA vs SLO vs SLI

These three terms are related, but they mean different things. Knowing the difference helps you define what to measure, aim for, and promise your customers.

Here's how they actually connect:

- SLI (Service Level Indicator): This is the metric you're measuring. For a login service, it could be the ratio of successful login requests to total valid requests. It tells you how your service is performing right now.

- SLO (Service Level Objective): You take that SLI and define a target around it. Something like "login availability should stay above 99.9% over a rolling 28-day window." When you're missing your SLO, it’s a signal to find out what's failing before customers notice.

- SLA (Service Level Agreement): This is what you promise your customers in a contract. It's usually set lower than the SLO, say 99.5% monthly availability. If you breach it, you owe service credits.

If your SLO and SLA are both set to 99.9%, then the moment your availability drops below 99.9%, you've already breached the agreement.

The SLI tells you where you stand. The SLO tells you where you should be. The SLA tells your customers what they can expect.

Over to you: How do you decide what the right SLO target is when you're launching a new service?

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API Concepts Every Software Engineer Should Know Most engineers use APIs every day. Sending a request and reading JSON is one thing. Designing an API that other people can rely on is something where things get complicated. A lot of problems begin with basic HTTP details that seem small at first. Methods, status codes, request formats, and response structure can make an API feel clear and predictable, or confusing and inconsistent. Then there are the bigger design choices. REST, GraphQL, gRPC, we...

ByteByteGo — Thu, 23 Apr 2026 09:23:38 GMT

API Concepts Every Software Engineer Should Know

Most engineers use APIs every day. Sending a request and reading JSON is one thing. Designing an API that other people can rely on is something where things get complicated.

A lot of problems begin with basic HTTP details that seem small at first. Methods, status codes, request formats, and response structure can make an API feel clear and predictable, or confusing and inconsistent.

Then there are the bigger design choices. REST, GraphQL, gRPC, webhooks, and WebSockets each make sense in different situations. The challenge is knowing what actually fits the system and the use case.

A lot of API problems also comes from design decisions that do not get enough attention early on. Naming, pagination, versioning, error responses, and backward compatibility often decide whether an API is easy to work with or frustrating to maintain.

Security is another area where weak decisions can cause real problems. API keys, OAuth, JWTs, scopes, and permissions are easy to mention. Getting them right is harder, and mistakes here can be costly.

Reliability matters too. Timeouts, retries, idempotency, rate limits, and caching are often easy to ignore until the system is under pressure.

And once an API starts growing, the supporting work matters too. Clear documentation, solid specs, observability, and contract testing make it much easier for teams to trust the API and use it without guessing how it works.

Over to you: What’s the most overlooked API concept in your experience?

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We're building a new course, Build with Claude Code, and we'd love your input before we finalize it. If you're an engineer or engineering leader, we'd appreciate 3 minutes of your time. Your answers will directly shape what we cover. Thank you so much! Take the short survey:

ByteByteGo — Thu, 23 Apr 2026 09:23:38 GMT

We're building a new course, Build with Claude Code, and we'd love your input before we finalize it.

If you're an engineer or engineering leader, we'd appreciate 3 minutes of your time. Your answers will directly shape what we cover. Thank you so much!

Take the short survey: https://docs.google.com/forms/d/e/1FA...

API Concepts Every Software Engineer Should Know Most engineers use APIs every day. Sending a request and reading JSON is one thing. Designing an API that other people can rely on is something where things get complicated. A lot of problems begin with basic HTTP details that seem small at first. Methods, status codes, request formats, and response structure can make an API feel clear and predictable, or confusing and inconsistent. Then there are the bigger design choices. REST, GraphQL, gRPC, we...

ByteByteGo — Thu, 23 Apr 2026 09:23:38 GMT

Data Warehouse vs Data Lake vs Data Mesh Storing data is the easy part. Deciding where and how to organize it is the real challenge. A data warehouse is the traditional approach. It cleans and structures data before storing it. Queries run fast, and reports stay consistent. But adding a new data source takes effort because everything has to fit the schema first. A data lake takes the opposite approach. It stores everything raw, like databases, logs, images, and video. Process it when you need it...

ByteByteGo — Wed, 22 Apr 2026 09:23:38 GMT

Data Warehouse vs Data Lake vs Data Mesh

Storing data is the easy part. Deciding where and how to organize it is the real challenge.

A data warehouse is the traditional approach. It cleans and structures data before storing it. Queries run fast, and reports stay consistent. But adding a new data source takes effort because everything has to fit the schema first.

A data lake takes the opposite approach. It stores everything raw, like databases, logs, images, and video. Process it when you need it. The flexibility is great, but if rules around naming, formatting, and ownership are not properly set, you end up with duplicate, outdated, and undocumented data that is hard to manage.

Data mesh shifts data ownership from a central team to individual departments. For example, sales publishes sales data, and finance publishes finance data. Shared standards keep things compatible across teams.

It works well in larger organizations. But it requires every team to have the right people and processes to manage their data quality, documentation, and access, which is a challenge.

In practice, many companies use more than one approach. They'll use a warehouse for dashboards and reporting, a lake for machine learning workloads and start applying mesh principles as teams scale.

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