DEV Community: Muhammad Furqan Ul Haq

Google Antigravity vs OpenAI Codex - Which AI coding agent is better?

Muhammad Furqan Ul Haq — Sat, 11 Jul 2026 17:24:07 +0000

Google Antigravity and OpenAI Codex are the two most capable AI coding agents in wide use in 2026, and they answer the same question in opposite ways: where should an autonomous coding agent live and do its work. Antigravity rebuilds the IDE around agents and keeps the developer in the loop visually. Codex moves the agent into a cloud sandbox and returns a finished pull request.

Both tools handle the full loop of planning, writing, running, and iterating on code with limited supervision. Capability is no longer the deciding factor between them. Architecture, workflow, and cost under sustained use are what separate them in practice.

This comparison examines both tools across the dimensions that shape daily engineering work: execution model, supported models, interaction style, GitHub integration, pricing, and measured performance. It closes with clear guidance on when each tool is the right default.

One structural point frames everything below. An agent reasons only over the context it can access, and neither tool captures a full-system view on its own. That limitation, and how teams close it with a context layer such as Bito's AI Architect, is covered before the conclusion. The direct comparison comes first.

Quick comparison: Antigravity vs Codex

Dimension	Google Antigravity	OpenAI Codex
Type	Agent-first development platform (VS Code fork)	Cloud-native coding agent across surfaces
Surfaces	Desktop IDE, CLI, SDK, Managed Agents API	CLI, IDE extension, desktop app, ChatGPT
Default model	Gemini 3.5 Flash	GPT-5.6-Codex family
Model choice	Gemini, Claude Sonnet, GPT-OSS	OpenAI models only
Where it runs	Local editor plus built-in browser	Isolated cloud sandbox
Interaction	Synchronous, real-time, visual	Asynchronous, batch, PR-based
Parallelism	Manager view, dynamic subagents	Parallel sandboxes with git worktrees
UI and browser testing	Yes, built-in Chromium subagent	Limited by sandbox, network-restricted
Verification	Artifacts: plans, screenshots, recordings	Diffs plus terminal logs
GitHub model	IDE source control	Native clone to diff to PR pipeline
Open source	CLI and SDK partial	CLI fully open source (Rust)
Entry pricing	Free public preview, then Google AI plans	Bundled in ChatGPT plans, then credits
Best for	Exploration, UI work, Google ecosystem	Locked specs, batch tasks, GitHub teams

Pricing and model names change frequently in this category. Treat the table as a mid-2026 reference and confirm current details on each vendor's page before committing budget.

Google Antigravity

Google introduced Antigravity in November 2025 alongside Gemini 3 and relaunched it as Antigravity 2.0 at Google I/O on May 19, 2026. The second version is the one to evaluate.

Antigravity is a development platform, not just an editor. It is a fork of VS Code with an agent layer built into the core of the product. It ships as a desktop app, a CLI written in Go, a public SDK, and a Managed Agents API inside the Gemini API. Google frames it as agent-first development, where the agent is a first-class participant rather than a sidebar assistant.

The interface has two surfaces. The Editor view is a conventional IDE with tab completion and inline commands. The Manager view inverts the relationship: instead of an agent embedded in a surface, the surfaces are embedded in the agent. From the Manager view, a developer launches and orchestrates several agents working in parallel across the editor, terminal, and browser.

Verification through artifacts is the defining feature. When an agent finishes work, it produces task lists, implementation plans, screenshots, and browser recordings. These give the developer verifiable proof of what the agent did without requiring a line-by-line read of raw tool calls. Google built this to address the trust problem inherent in autonomous execution.

Antigravity runs Gemini 3.5 Flash by default. Google reports Flash as roughly four times faster than Gemini 3.1 Pro while matching it on most benchmarks. Developers can also route tasks to Claude Sonnet or the open-weight GPT-OSS models inside the same agent. This model flexibility is a genuine advantage over single-vendor tools.

OpenAI Codex

Codex in 2026 has no relation to the 2021 autocomplete model of the same name. It is an umbrella for several surfaces that share one account, one configuration, one agent engine, and one set of rate limits.

Those surfaces are the open-source CLI written in Rust, a desktop app for macOS and Windows, a VS Code and JetBrains extension, and a cloud agent inside ChatGPT. As of July 2026, Codex is also integrated directly into the ChatGPT desktop app. The CLI has passed 67,000 GitHub stars and 400 contributors, which reflects strong adoption among terminal-first developers.

The core workflow is cloud-native. A developer assigns a task, and Codex clones the repository into an isolated sandbox, reads the codebase, edits files across the tree, runs the test suite, iterates on failures, and opens a pull request. The developer reviews the diff and terminal logs, then merges. Tasks run from one minute to several hours depending on scope.

Codex runs on the GPT-5.6-Codex family as of mid-2026, tuned specifically for agentic software engineering. It reads an AGENTS.md file in the repository to carry project conventions and test commands. That file should stay stable within a session, because editing it mid-run invalidates the cached prefix that keeps token costs down.

Two further capabilities matter for teams. Skills let Codex learn an organization's standards and apply them consistently across tasks with less supervision. Scheduled tasks let it handle routine work such as issue triage, alert monitoring, and CI on a set cadence.

The defining difference: local IDE vs cloud sandbox

Every other distinction follows from one architectural choice.

Antigravity keeps the developer close to the work. The agent operates against the local environment, moves visibly across the editor and a built-in Chromium browser, and accepts mid-task steering through comments on its artifacts. This is a supervised, high-visibility model where reasoning is observable as it happens.

Codex removes the developer from the loop during execution. The agent works in an isolated cloud sandbox, separated from the live machine. That separation delivers safety and clean parallelism, but it reduces interactivity. Codex does not naturally pause to ask a clarifying question, and its network-restricted sandbox cannot browse the open web or drive a live browser the way Antigravity can.

The result is a clean division. Antigravity suits work where requirements are still forming and visual verification matters. Codex suits work where the specification is settled and tasks can be queued and reviewed later.

Models and reasoning

Model quality has converged at the top of this category, so architecture and workflow now matter more than raw model scores.

Antigravity defaults to Gemini 3.5 Flash for speed and dispatches parallel subagents for throughput. Its multi-model support lets a developer assign the same task to Gemini, Claude Sonnet, or GPT-OSS and compare, which is useful when one model handles a domain better than another.

Codex commits to OpenAI's own models, currently the GPT-5.6-Codex family. The trade-off is depth over breadth. The model is tuned tightly to the Codex harness, and developers consistently report strong whole-codebase comprehension, including the agent flagging that a function is used in several other places and updating all of them without being asked.

For dependency-heavy, cross-file work, Codex's codebase reading is an asset. For work that benefits from choosing the strongest model per task, Antigravity's optionality wins.

Workflow: synchronous vs asynchronous

Daily workflow diverges most clearly here.

Antigravity encourages a hands-on rhythm. The developer launches a task, watches the plan form, reviews artifacts, and corrects course in real time. The Manager view supports several concurrent agents, but the mental model stays supervised and immediate.

Codex encourages batching. A common pattern is queuing four or five well-scoped tasks at the start of the day, then reviewing the completed pull requests later. Each task runs in its own git worktree, so parallel work merges cleanly without conflicts.

Neither approach is superior in the abstract. Live supervision favors Antigravity. Fire-and-review favors Codex.

GitHub integration and verification

Codex is built around the pull request. Its entire flow is structured as assign task, clone repository, work in sandbox, open PR, review diff, merge. For teams that run GitHub as their hub and value asynchronous, reviewable delivery, this is a natural fit.

Antigravity handles git through the IDE, the way any VS Code editor does. Agents stage, commit, and branch through the source control panel. It has no native PR-creation surface that matches the clone-to-diff-to-PR pipeline in Codex. In exchange, it produces richer intermediate output: plans, execution traces, browser screenshots, and recordings that a developer can inspect at each step.

The two verification models sit at different points in time. Codex builds trust after execution, through a clean diff and logs. Antigravity builds trust during execution, through visible artifacts. Teams that prefer to audit reasoning as it happens lean toward Antigravity. Teams that prefer to review a finished, self-contained change lean toward Codex.

Pricing and cost of ownership

Both tools use consumption-based economics, so cost scales with usage rather than sitting flat.

Antigravity is free in public preview, which makes it the lowest-friction entry point. Paid usage scales through Google AI Pro at $20 per month and Google AI Ultra at roughly $99.99 per month. Its quota model splits into prompt credits for input and flow credits for agent execution. Running many parallel agents in the Manager view consumes flow credits quickly, so its most powerful workflow is also its most expensive. Google tightened quotas and moved to credit packs earlier in 2026, which disrupted some early users, so quota limits are worth monitoring.

Codex carries no separate line item for most users. It rides on ChatGPT plans, which is a major reason it reached more than four million weekly developers by late May 2026. ChatGPT Plus at $20 per month covers occasional use. Heavy, parallel, large-repository work pushes teams to Pro at $200 per month or to Team and Enterprise plans. Codex moved to token-based credit billing in April 2026, roughly 5 to 45 credits per task on the frontier model, with cached input costing about a tenth of fresh input. Notably, Codex uses meaningfully fewer tokens per task than most competitors, which lowers effective cost on routine work.

In short, Antigravity is cheaper to start and cost-efficient under controlled use. Codex is predictable for teams already on ChatGPT, though heavy parallel use meters up quickly.

Performance and benchmarks

Benchmark gaps among the leading agents narrowed through 2026, so a single percentage point rarely decides a real workflow.

Codex posts strong SWE-bench results in the high 80s and leads on terminal-focused benchmarks. It optimizes for speed and token efficiency, which means complex multi-file refactors sometimes need an extra review pass before merging. Independent blind reviews have favored other frontier agents over Codex on the hardest tasks, so first-pass output on complex work warrants a review cycle.

Antigravity leans on Gemini 3.5 Flash for speed and on parallel subagents for throughput, and it leads the MCP Atlas tool-use benchmark, which measures how well it coordinates tools. Speed is only useful when the output is correct, so the artifact review step remains important.

The practical read is consistent across independent testing: no single agent leads on every task type. Match the tool to the work.

Choosing between them

Choose Google Antigravity when the work involves fuzzy requirements, in-browser UI testing, or visual verification, when model choice across Gemini, Claude, and GPT-OSS is valuable, when the team already operates inside Google's ecosystem, or when a free starting point matters.

Choose OpenAI Codex when the specification is locked and implementation speed is the priority, when the team runs GitHub and values asynchronous reviewable pull requests, when well-scoped maintenance tasks can be batched and parallelized, when ChatGPT is already the team's platform, or when an open-source CLI that can be read and customized is a requirement.

Many teams run both. A practical split assigns exploration and verification to Antigravity and locked-spec implementation to Codex. One tool investigates and validates; the other ships.

The context limitation both tools share

Both agents reason from what fits in their context window. That window includes the files in the session and, at best, the current repository. It does not include how an authentication service depends on a specific cache configuration, that a past architecture decision ruled out an approach, or that a downstream service has rate limits that caused a production incident months earlier.

That missing system-level context is where generated code moves from adequate to production-ready, and it is where many agentic failures originate.

Bito's AI Architect targets exactly this gap. It builds a live knowledge graph of the entire engineering system, ingesting code across all repositories along with Jira and Linear tickets, Confluence documentation, Slack discussions, and observability data. Rather than retrieving similar snippets through vector search, it models how services, APIs, and dependencies actually connect.

The point relevant to this comparison is that AI Architect is not a competing agent. It connects to Antigravity and Codex over MCP. Its installer auto-configures the Google Antigravity (IDE + agy CLI) and OpenAI Codex CLI, and it works with any MCP client, so the same grounding applies regardless of which agent a team standardizes on.

The measured effect is direct. On large, real-world codebases, supplying an agent with system context through AI Architect raised task success from roughly 44 percent to 61 percent while cutting token cost by close to half. That token reduction is material here, because both Antigravity and Codex bill by consumption, and better context means fewer wasted turns.

For any serious adoption of either tool, the context layer deserves evaluation alongside the agent itself. The agent determines how fast a team moves. The context determines whether the output is correct.

Conclusion

Google Antigravity and OpenAI Codex reflect two coherent strategies for agentic development.

Antigravity places agents in a dedicated, visible workspace where developers watch, verify, and steer through artifacts. It is the stronger default for exploration, UI work, model flexibility, and teams inside Google's ecosystem.

Codex places agents in the cloud, running asynchronously and delivering clean pull requests for review on the developer's schedule. It is the stronger default for locked specifications, GitHub-centric teams, batch parallelism, and organizations already committed to ChatGPT.

Neither tool wins outright, and few teams should limit themselves to one. The durable approach is to match the agent to the task, run both where budget allows, and ground whichever agent handles the work with real system context. Speed moves a team forward. Context is what keeps the code correct in production.

7 Open-Source Codebase Context Tools for Engineering Teams

Muhammad Furqan Ul Haq — Fri, 03 Jul 2026 19:40:54 +0000

Your AI coding agent starts every session blind. Ask Claude Code, Cursor, or Codex a question about your repo and it does the same thing a new hire would: grep, glob, open a file, read 800 lines, open another file, repeat. That discovery loop burns tokens, wastes time, and still misses cross-file relationships that don't show up in a text search.

Codebase context tools fix this. They index your code once into something queryable (a knowledge graph, a vector index, or a virtual filesystem) and expose it to your agent, usually over MCP. The agent asks a targeted question and gets the exact code back instead of scanning for it. Fewer tokens, fewer tool calls, better first-attempt answers.

Below are seven open-source options, grouped by how they actually work. Each entry covers what it does, how it works, where it shines, and where it stops. Star counts are approximate and as of writing.

Code knowledge graphs

These parse your source into symbols and relationships (calls, imports, inheritance) and let the agent walk the graph.

1. CodeGraph

~57.3k stars · MIT · TypeScript · 100% local

CodeGraph builds a pre-indexed knowledge graph of your codebase and serves it to agents through an MCP server. Everything stays on your machine. It uses tree-sitter to parse code into a local SQLite database with FTS5 full-text search. No API keys, no cloud.

It's genuinely zero-config. Install, wire up your agents, index a project:

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh
codegraph install        # auto-detects and configures your agents
cd your-project
codegraph init -i        # creates the index and builds the graph

The installer auto-detects Claude Code, Cursor, Codex CLI, opencode, Gemini CLI, Kiro, and more. A native OS file watcher keeps the graph fresh as you edit, with a debounce window and a staleness banner so the agent never gets a silently wrong answer.

The standout features are the graph queries: codegraph_explore answers "how does X work" in one call, and codegraph_impact traces the blast radius of a symbol before you change it. It supports 20+ languages and does framework-aware route detection (Django, FastAPI, Express, NestJS, Spring, Rails, and others) plus cross-language bridging for mixed iOS / React Native codebases. The project's own benchmarks report roughly 16% lower cost and 58% fewer tool calls versus a bare agent.

Best for: teams that want a fast, local, no-nonsense structural index for their coding agents, especially large or polyglot repos.

Watch out for: it's purely structural code context. It knows your call graph, not why the code exists.

Repo: https://github.com/colbymchenry/codegraph

2. CodeGraphContext (CGC)

~3.9k stars · MIT · Python

CGC is both an MCP server and a standalone CLI. It indexes local code into a graph database and lets you query relationships in plain English through your agent, or directly from the terminal.

pip install codegraphcontext
codegraphcontext mcp setup   # wizard configures your IDE/agent
codegraphcontext index .

The differentiator is backend flexibility. It ships with embedded databases (FalkorDB Lite, KuzuDB, LadybugDB) for zero-config local use, and scales up to Neo4j for large graphs. It supports 23 languages via tree-sitter, with optional SCIP indexers (scip-clang, scip-dotnet) for more accurate C/C++/C# call and inheritance resolution.

Beyond callers/callees and class hierarchies, CGC does code-quality analysis: dead-code detection, cyclomatic complexity, and full call-chain tracing across hundreds of files. It also has a live file watcher and a premium interactive HTML visualization of the graph.

Best for: developers who want CLI-first code analysis (complexity, dead code, call chains) as much as agent context, and who may already run Neo4j.

Watch out for: the graph-database setup adds moving parts compared to a single-file index, and heavier backends mean more to operate.

Repo: https://github.com/CodeGraphContext/CodeGraphContext

3. Graphify

~76.9k stars · MIT · Python (YC-backed)

Graphify is the broadest of the graph tools. It installs as a skill in your AI assistant: you type /graphify . and it maps your project into a knowledge graph you can query instead of grepping.

uv tool install graphifyy   # note: package is "graphifyy"
graphify install            # registers the skill with your assistant
# then, inside your assistant:
/graphify .

The twist: it doesn't stop at code. Graphify ingests code (36 tree-sitter grammars, parsed locally with no API calls), plus SQL schemas, docs, PDFs, images, and even videos, so app code, database schema, and infrastructure end up in one graph. Code extraction is local; everything non-code goes through your assistant's model.

You get three artifacts: an interactive graph.html, a GRAPH_REPORT.md with "god nodes" and surprising cross-file connections, and a graph.json you can query anytime. It uses Leiden community detection to cluster your codebase, commits the graph to git so the whole team shares one map, and can run as an MCP server over stdio or HTTP.

Best for: teams that want a queryable map spanning code and surrounding artifacts (schemas, docs, papers), with a shared graph checked into git.

Watch out for: the multi-modal extraction (docs, PDFs, images) uses your model API and can cost tokens; a code-only graph stays free and local.

Repo: https://github.com/safishamsi/graphify

Retrieval and memory engines

These lean on embeddings and semantic search rather than a pure graph, and add cross-session memory.

4. Code Context Engine (CCE)

~260 stars · MIT · Python · local-first

CCE takes the retrieval approach: it indexes your code into vector embeddings and serves the relevant chunks instead of whole files. One command sets it up:

uv tool install code-context-engine
cd /path/to/your/project
cce init   # index, install hooks, register MCP server

Under the hood it's a hybrid retriever: vector similarity plus BM25 keyword matching fused with Reciprocal Rank Fusion, then graph expansion along CALLS/IMPORTS edges to pull in related code. Chunks are tree-sitter AST-aware (Python, JS/TS, PHP, Go, Rust, Java) and compressed to signatures + docstrings. It stores everything in a couple of SQLite files via sqlite-vec, so the install stays small and runs on CPU.

Two things set it apart. First, cross-session memory: record_decision("use JWT for auth", reason="...") persists to SQLite and surfaces via session_recall next session, so you stop re-explaining your architecture. Second, it's security-conscious by default: it skips secret files, scans content for leaked keys, and scrubs PII from memory writes. The project reports ~94% retrieval token savings benchmarked on FastAPI, with a live dashboard and dollar-cost tracking.

Best for: solo devs and small teams who want measurable token savings, semantic search, and persistent decisions without running a graph DB.

Watch out for: it's early and small. Fewer languages have full AST chunking, and adoption/community is still building.

Repo: https://github.com/elara-labs/code-context-engine

5. Bitloops

~230 stars · Apache-2.0 · Rust · local-first

Bitloops reframes the problem. Instead of only indexing code structure, it's a memory and context layer that captures agent reasoning alongside your repository. When code changes, it records the developer–agent workflow around each commit, so reviewers see not just the diff but how the change was produced.

curl -fsSL https://bitloops.com/install.sh | bash
bitloops init --install-default-daemon

Three ideas anchor it: repository memory shared across supported agents, targeted context retrieval (retrieve relevant code + prior reasoning instead of dumping the repo), and Git-linked reasoning capture for traceability and governance. It ships a local observability dashboard, and it can ingest external knowledge by URL: GitHub issues and PRs, Jira tickets, and Confluence pages linked to your repo context. Queries run through DevQL, a typed GraphQL interface over artifacts, checkpoints, and knowledge. It's local-first (SQLite/Postgres + DuckDB/ClickHouse) and agent-agnostic (Claude Code, Codex, Cursor, Gemini, Copilot, OpenCode).

Best for: teams that care about why: auditing AI-assisted changes and keeping agent reasoning searchable across sessions and reviewers.

Watch out for: it's early (small releases, low stars) and its value depends on adopting the git-linked capture workflow across the team.

Repo: https://github.com/bitloops/bitloops

Broader agent context databases

These aren't code-specific. They manage context for agents in general, which makes them powerful and heavier.

6. OpenViking

~26.3k stars · AGPL-3.0 · Python/Rust (by Volcengine)

OpenViking is a "context database" for AI agents. It abandons flat vector storage and organizes memory, resources, and skills as a virtual filesystem under a viking:// protocol, so agents ls, find, and grep context like files.

pip install openviking --upgrade
openviking-server init   # interactive setup, can use local Ollama models
openviking-server

Its design solves problems the code-only tools don't touch: tiered L0/L1/L2 loading (a one-line abstract, an overview, then full detail) to cut tokens; directory recursive retrieval that locates a high-scoring directory before refining inside it; a visualized retrieval trajectory so you can debug why something was retrieved; and automatic session management that extracts long-term memory so the agent gets smarter with use. It needs both a VLM and an embedding model (Volcengine, OpenAI, or LiteLLM-compatible providers).

Best for: teams building agents that need managed long-term memory and resources, not just a static code index, with plugins for OpenClaw, OpenCode, and Claude Code memory.

Watch out for: it's the heaviest option here (a server plus model dependencies), and it's general-purpose context, not a code graph. The AGPL-3.0 license also matters for some commercial use.

Repo: https://github.com/volcengine/OpenViking

7. Airweave

~6.5k stars · MIT · Python (FastAPI)

Airweave is a context retrieval layer that connects your apps, tools, and databases, syncs them continuously, and exposes everything through one LLM-friendly search interface. Agents query it via SDK, REST, MCP, or CLI to get grounded, up-to-date context from many sources at once.

git clone https://github.com/airweave-ai/airweave.git
cd airweave
./start.sh   # Docker + docker-compose; or use the hosted cloud

It ships 50+ integrations (Confluence, Jira, Linear, Notion, Slack, GitHub, GitLab, Gmail, Google Drive, Salesforce, HubSpot, and more) and handles auth, ingestion, syncing, indexing, and retrieval so you don't rebuild pipelines per agent. The stack reflects its scope: PostgreSQL for metadata, Vespa for vectors, Temporal for orchestration, Redis for pub/sub, Kubernetes for prod.

Best for: teams that need agents to retrieve across business data sources (tickets, docs, CRM, chat), not just source code.

Watch out for: it isn't a code-graph tool. It won't give you callers/callees or a call chain; it's a unified RAG layer over many SaaS sources, and it's the most infrastructure-heavy to self-host.

Repo: https://github.com/airweave-ai/airweave

Quick comparison

Tool	Approach	Runtime	Storage	Best fit
CodeGraph	Code knowledge graph	Local	SQLite + FTS5	Fast local structural context
CodeGraphContext	Code knowledge graph	Local/server	FalkorDB/Kuzu/Neo4j	CLI analysis + graph queries
Graphify	Multi-modal graph	Local + model	JSON/HTML graph	Code + docs + schema map
Code Context Engine	Hybrid retrieval + memory	Local	sqlite-vec	Token savings + decisions
Bitloops	Reasoning memory layer	Local	SQLite/DuckDB	Auditing AI-assisted changes
OpenViking	Agent context database	Server + models	Filesystem paradigm	Managed long-term agent memory
Airweave	Multi-source retrieval	Server (Docker/K8s)	Postgres + Vespa	Context across business apps

Rough decision guide: want a drop-in local code graph? Start with CodeGraph or CodeGraphContext. Want code plus docs and schema in one map? Graphify. Want semantic search with persistent decisions? CCE. Want to audit why agents changed things? Bitloops. Building a general agent that needs managed memory or many data sources? OpenViking or Airweave.

Where these tools stop

Notice the pattern. Six of the seven index your code. They make your agent great at structural questions: who calls this, what breaks if I change it, where is this defined. That's real value, and if code structure is your only gap, pick one and move on.

But most of what makes a codebase hard to understand isn't in the code. It's the Jira ticket that explains why a weird workaround exists. The Slack thread where the team decided to drop a feature flag. The Confluence design doc, the Google Doc spec, the incident in your observability tool that made someone add that retry loop. A pure code graph can't see any of it, so your agent still guesses at intent.

There's a second gap: reach. These tools mostly feed coding agents. They don't help the ChatGPT or Claude chat window where a PM asks "how does billing work," and they don't plug into code review, where context matters most.

This is the space Bito's AI Architect works in. It builds a knowledge graph of your codebase, then connects the context around it: coding agents (Claude Code, Cursor), issue trackers (Jira, Linear), Slack, Confluence and Google Docs, and observability tools, plus custom instructions so it follows your team's conventions. That same context feeds chat agents (ChatGPT, Claude) and Bito's AI Code Review Agent, not just your IDE. The trade-off is scope: it's a broader, integration-driven layer rather than a single local index, so it fits teams whose real bottleneck is scattered knowledge across many systems, not just call graphs.

If your agent already writes correct code but keeps missing the why, that's the gap worth closing, whether with one of the open-source tools above, a broader layer like Bito's AI Architect, or a combination.

Takeaways

AI agents waste most of their budget on discovery. A context index removes that.
For local, code-only structure, CodeGraph, CodeGraphContext, and Graphify are the strongest open-source graph options; CCE and Bitloops add retrieval and memory.
OpenViking and Airweave solve a bigger problem, general agent context, at the cost of more infrastructure.
No code-only tool captures the reasoning, tickets, docs, and signals that explain why your code looks the way it does. Decide whether that gap matters for your team before you pick.

Try one on a real repo this week and measure the token difference yourself. That's the only benchmark that counts.

15 Best GitHub Code Review Tools [Free & Paid]

Muhammad Furqan Ul Haq — Mon, 18 Nov 2024 04:55:32 +0000

Code reviews can feel like a double-edged sword. As a developer, I’ve spent countless hours combing through pull requests, juggling deadlines, and trying to provide meaningful feedback. While necessary for maintaining quality, the process often becomes tedious and time-consuming, especially when you’re dealing with complex codebases or tight schedules.

Code reviews are more than just a task—they’re a critical step in ensuring quality, improving collaboration, and avoiding costly bugs. But the traditional approach to code reviews often falls short, bogged down by manual effort, lack of context, and subjective biases.

Thankfully, modern code review tools are stepping up to solve these challenges. From automated checks to AI-powered assistants, these tools can identify bugs, improve code quality, and even suggest fixes—all while saving you time.

In this article, we’ll explore the 15 best GitHub code review tools available today. Whether you’re looking for an AI-powered assistant or a collaborative review platform, this guide will help you choose the right tool for your needs.

1. Bito's AI Code Review Agent

Bito’s AI Code Review Agent is transforming code reviews by understanding your entire codebase and providing highly accurate, context-aware feedback. It doesn’t just spot bugs and vulnerabilities; it also identifies inefficiencies and suggests actionable fixes.

Key features:

Context-aware reviews that consider the entire codebase.
Detailed feedback on pull requests, highlighting key issues.
AI-driven suggestions for improving code quality and maintainability.
Detailed code review analytics.
Integration with GitHub, GitLab, Bitbucket, VS Code, and JetBrains IDEs for seamless workflows.

Pricing details:

Bito offers a free trial, with paid plans starting at $15 per user per month.

Learn more

Unlock the power of Bito’s AI for free through their Open-Source Sponsorship Program. Apply now to gain free access to Bito’s AI Code Review Agent.

Apply for free Bito sponsorship program

2. CodeRabbit

CodeRabbit uses AI to analyze pull requests in real time, providing detailed, unbiased feedback. It enhances collaboration by surfacing potential issues early and ensuring consistent code quality.

Key features:

AI-powered analysis of pull requests.
Detection of bugs, vulnerabilities, and performance issues.
Integrates directly with GitHub for smooth collaboration.
Customizable rules for specific project needs.
Automated suggestions for refactoring.

Pricing details:

CodeRabbit offers a free trial, with paid plans starting at $15 per user per month.

Learn more

3. Qodo Merge (formerly PR-Agent)

This AI-driven tool focuses on simplifying code reviews by categorizing changes and providing clear suggestions. It’s designed to save time for reviewers while maintaining high standards.

Key features:

Categorized review suggestions for faster assessments.
Highlights key changes and their impact on the codebase.
Integrates directly into GitHub workflows.
AI-driven recommendations for code improvements.
Easy setup with minimal configuration.

Pricing details:

Free for small teams; premium plans start at $19 per user per month.

Learn more

4. GitHub's built-in code review

GitHub’s native code review system is a staple for many teams, offering intuitive features to comment on specific lines, request changes, and track feedback.

Key features:

Inline commenting on pull requests.
Approval and change request workflows.
Seamless integration with GitHub repositories.
Notifications and activity tracking.
Support for draft pull requests.

Pricing details:

Free for public repositories and $4 per user per month for private ones.

Learn more

5. Review Board

Review Board is an open-source, web-based code review tool that integrates with various version control systems, making it easy for teams to collaborate on reviews.

Key features:

Web-based review interface.
Multi-repository support.
Automated issue tracking and resolution.
Syntax highlighting for easier reading.
Historical record of reviews.

Pricing details:

Self-hosted solution is free whereas cloud-based plan starts at $29 per month.

Learn more

6. Crucible

Crucible enables detailed peer reviews, allowing teams to discuss and resolve issues collaboratively before merging code.

Key features:

Inline comments and threaded discussions.
Deep integration with GitHub and other version control systems.
Customizable workflows and permissions.
Supports pre-commit reviews.
Tracks metrics for team performance.

Pricing details:

Starts at $10 one-time payment.

Learn more

7. CodeScene

CodeScene focuses on technical debt and code health, providing actionable insights that help maintain clean, efficient codebases.

Key features:

Identifies hotspots and technical debt.
Visualizes code quality trends over time.
Prioritizes issues based on their impact.
Integrates with CI/CD pipelines.
Offers risk analysis for changes.

Pricing details:

Plans start at €20 per active author per month.

Learn more

8. Codacy

Codacy is an automated code review platform that helps developers ensure code quality by performing static analysis and providing actionable insights on code style, complexity, and test coverage.

Key features:

Automated static code analysis.
Real-time feedback on code issues.
Integration with GitHub for seamless pull request checks.
Tracks technical debt and code coverage trends.
Customizable rules for project-specific needs.

Pricing details:

Free for open-source projects; team plan starts at $18 per user per month.

Learn more

9. SonarCloud

SonarCloud helps identify bugs, vulnerabilities, and code smells in your codebase. It integrates with GitHub to provide clear, actionable feedback on pull requests, improving code maintainability.

Key features:

Cloud-based static analysis tool.
Highlights vulnerabilities and security hotspots.
Supports multiple languages and frameworks.
Direct integration with GitHub for pull request analysis.
Tracks code quality metrics over time.

Pricing details:

Free for open-source projects; team plan starts at $32 per month.

Learn more

10. Semgrep

Semgrep is a lightweight static analysis tool that allows you to write custom rules for code scanning. It’s perfect for teams looking for a flexible and efficient code review solution.

Key features:

Detects security vulnerabilities and code issues.
Highly customizable with user-defined rules.
Integrates directly with GitHub for real-time feedback.
Lightweight and easy to set up.
Supports multiple languages and frameworks.

Pricing details:

Free for basic usage; paid plans start at $40 per user per month.

Learn more

11. Qodana

Developed by JetBrains, Qodana is a code quality platform that integrates with CI/CD pipelines to enforce coding guidelines and assist in code reviews.

Key features:

Advanced static code analysis.
Detects code smells and anti-patterns.
Integrates with GitHub and CI/CD pipelines.
Supports over 15 programming languages.
Offers detailed reports with actionable insights.

Pricing details:

Free for small teams; premium plans start at $6 per user per month.

Learn more

12. PullRequest

PullRequest offers professional, on-demand code reviews by vetted experts. It ensures high-quality reviews without adding to your team’s workload.

Key features:

On-demand code reviews by experts.
Detailed feedback on bugs, vulnerabilities, and improvements.
Seamless integration with GitHub.
Comprehensive reviewer matching based on expertise.
Support for multiple programming languages.

Pricing details:

Starting at $129 per developer per month (billed annually)

Learn more

13. CodeFactor

CodeFactor monitors code quality in real time and integrates with GitHub to provide actionable feedback directly on pull requests.

Key features:

Continuous monitoring of code quality.
Real-time feedback on pull requests.
Detects complexity, style issues, and code smells.
Integration with GitHub and CI/CD tools.
Tracks technical debt and improvement trends.

Pricing details:

Free for open-source projects; team plans start at $24 per month.

Learn more

14. DeepCode AI

DeepCode AI is an AI-powered code analysis tool that scans codebases for bugs and vulnerabilities. Its recommendations help maintain high-quality, secure code.

Key features:

AI-driven code scanning and analysis.
Detects bugs, vulnerabilities, and inefficiencies.
Integrates directly with GitHub pull requests.
Supports multiple programming languages.
Offers actionable suggestions for fixes.

Pricing details:

Free for individual developers; enterprise pricing available on request.

Learn more

15. CodeClimate

CodeClimate provides automated code review tools to measure test coverage, code maintainability, and other quality metrics, helping teams improve their codebases over time.

Key features:

Automated code review and quality checks.
Metrics for test coverage and maintainability.
Integration with GitHub for pull request feedback.
Historical analysis of code quality trends.
Supports multiple languages and frameworks.

Pricing details:

Free for open-source projects; team plans start at $20 per user per month.

Learn more

Conclusion

The world of code reviews is changing, and AI-powered tools are leading the way. These tools not only save time but also bring objectivity and consistency to the process. Whether you’re a solo developer or part of a large team, investing in the right code review tool can dramatically improve your workflow.

Among these tools, Bito’s AI Code Review Agent stands out as a comprehensive solution, offering unmatched context-aware suggestions, code review analytics, and seamless GitHub integration. However, the choice depends on your specific needs—whether it’s automation, collaboration, or enhanced code quality.

Explore these tools and embrace the future of code reviews today. Your team—and your codebase—will thank you.

Free AI-powered Code Reviews for Open-Source Software Projects

Muhammad Furqan Ul Haq — Thu, 31 Oct 2024 10:19:17 +0000

If you’re involved in open-source software, you know how crucial code reviews are. They’re not just about catching bugs—they ensure code quality, security, and maintainability, helping every contributor work together seamlessly. But let’s face it, code reviews are time-consuming. Reviewing every pull request (PR) manually can slow down development, especially in open-source projects where resources are limited.

Enter Bito’s AI Code Review Agent—an automated tool designed to take the heavy lifting out of code reviews. With Bito’s new Open-Source Sponsorship Program, you can now access this tool for free, gaining AI-powered insights for every PR on GitHub, GitLab, and Bitbucket. Imagine faster, more consistent reviews without the usual back-and-forth delays.

Apply now for free AI code reviews!

Why AI code reviews are a game-changer for open-source

Here’s how Bito’s AI Code Review Agent can help you manage and grow your OSS project:

Speed up code reviews: Quickly address potential issues and keep PRs moving forward with instant, AI-generated feedback.
Enhance code quality: Spot and fix bugs, security flaws, and performance issues early in the development cycle.
Simplify collaboration: Contributors get clear, concise feedback, making it easier to review and improve code without extensive back-and-forth.

How Bito’s AI can transform your open-source project

1- Automated, in-depth reviews for every PR

Imagine getting instant, accurate feedback on every pull request without waiting on busy contributors or maintainers. Bito’s AI automatically analyzes code changes, highlighting key areas with detailed suggestions. This means you can catch potential issues like code smells, security vulnerabilities, and bugs before they become bigger problems.

2- Seamless integration with GitHub, GitLab, and Bitbucket

Bito integrates directly into your workflow, generating clear summaries and suggestions for each PR that contributors can act on quickly. AI-generated insights mean you can maintain a high standard across the project without sacrificing speed.

3- Code review analytics

Bito provide valuable metrics like issues found, lines of code reviewed and individual contributor performance. This helps you stay on top of the project’s health and identify improvement areas, making your workflow more transparent.

4- Integrated static analysis tools for extra coverage

Beyond AI-driven reviews, Bito comes packed with built-in static analysis tools like Astral Ruff, Mypy, fbinfer, and Sonar, covering a range of potential code quality issues. This means deeper insights and fewer surprises in production—all without leaving your workflow.

5- Privacy you can trust

One key thing to know—Bito respects your code privacy. Your code is never stored or used to train AI models, so you get all the benefits of advanced AI without compromising your project’s security or integrity.

Ready to Get Started?

Applying is simple. Just fill out the application form with your project details, and Bito’s team will reach out to you. This is a golden opportunity for open-source maintainers looking to level up their workflow with cutting-edge AI support.

Apply now for free AI code reviews!

Let’s bring the power of AI to open-source projects, helping developers like you create, improve, and ship code faster than ever. I’m excited to see how Bito’s tools can support your next big release!

Top 13 VS Code Extensions to become a JavaScript Wizard

Muhammad Furqan Ul Haq — Fri, 11 Oct 2024 11:55:27 +0000

Writing code is hard. As a JavaScript developer, you’ve probably felt the pressure of juggling multiple tasks — writing clean code, debugging tricky issues, and keeping up with ever-changing project demands. The reality is that coding can quickly turn into a grind, with countless small problems slowing you down and stealing your focus.

But what if there was a way to make it easier?

In this article, I’m going to walk you through a set of essential VS Code extensions that have drastically improved my own productivity as a developer.

These tools help streamline everything from code quality checks to faster debugging and even managing API calls — basically, everything that usually eats up your time.

By the end, you’ll have a powerful toolkit to make your JavaScript development faster, smoother, and a whole lot more efficient.

Let’s get started!

1- Bito

Bito is a game-changer for developers looking to integrate AI-powered assistance directly into their development workflow. Unlike GitHub Copilot, which only provides suggestions based on open files, Bito’s AI has a comprehensive understanding of your entire codebase, enabling more accurate and context-aware assistance.

Bito’s free plan gives you access to GPT-4-like AI models, streamlining development tasks. From generating code snippets to automating code reviews, Bito delivers smart, high-quality suggestions, allowing you to focus on coding while reducing manual work.

Key features:

Chat with AI that understands your entire codebase.
Instant, in-line suggestions while you type.
AI-powered code review automation.
Command Line Interface (powered by Bito AI Chat) to automate your tasks.
User-friendly analytics dashboards to track team members’ contributions.

Install on VS Code

2- Quokka.js

Quokka.js is like having a live scratchpad inside VS Code. It’s an incredibly useful tool for quickly testing JavaScript code without setting up a full project environment. The real-time results allow you to instantly see the output of your code as you type, which can be a huge productivity boost when experimenting with new ideas or debugging tricky logic.

Key features:

Live feedback on code execution.
Instant value displays for variables and expressions.
Ability to evaluate code inline without a console.
Supports ES6 and TypeScript syntax.
Enhanced testing environment for quick prototyping.

Install on VS Code

3- Thunder Client

If you frequently work with APIs, Thunder Client is an intuitive alternative to Postman, but directly within your VS Code setup. It helps you test RESTful APIs without leaving the editor, keeping everything in one place and saving you from switching between apps.

Key features:

Simple and fast REST API testing within VS Code.
Supports GET, POST, PUT, DELETE requests.
Easy-to-manage collections of API requests.
Supports authentication headers and environment variables.
JSON and XML response formatting.

Install on VS Code

4- Prettier

Prettier is your go-to tool for automatically formatting your code. JavaScript can get messy quickly, and keeping code consistent across files is challenging. Prettier automatically enforces consistent styling rules, making your code cleaner and more readable without requiring manual intervention.

Key features:

Automatic code formatting for JavaScript, HTML, CSS, and more.
Enforces a consistent style across your entire project.
Supports many file types beyond JavaScript.
Works seamlessly with Git to auto-format before commits.
Customizable settings for code styling preferences.

Install on VS Code

5- Import Cost

Have you ever wondered how much impact your imported modules are having on your JavaScript bundle size? Import Cost helps you see the size of each import directly in your editor, giving you visibility into which dependencies are bloating your project and helping you make informed decisions about optimizing performance.

Key features:

Shows the size of imported libraries/modules in real-time.
Helps you identify large dependencies at a glance.
Works for JavaScript and TypeScript projects.
Visual alerts when an import exceeds a certain size threshold.
Helps optimize bundle size by suggesting lighter alternatives.

Install on VS Code

6- ESLint

Keeping your code error-free and consistent with best practices is essential, and ESLint is the tool to do just that. It helps you identify and fix coding errors, enforces coding standards, and ensures that your JavaScript remains maintainable across projects.

Key features:

Identifies syntax errors and code quality issues.
Enforces coding standards and best practices.
Easily customizable rule sets.
Provides real-time linting feedback in your editor.
Integration with Prettier to avoid style conflicts.

Install on VS Code

7- Live Server

Live Server gives you an instant preview of your web application by launching a local development server with live reload. This means that every time you save your changes, your browser will automatically refresh to show the updated page, making it a valuable tool for frontend development.

Key features:

Instant live preview of your web app in the browser.
Auto-reload on file save, speeding up your dev loop.
Supports custom port configurations.
Useful for testing static and dynamic content.
Works seamlessly with HTML, CSS, and JavaScript projects.

Install on VS Code

8- ES7+ React/Redux/React-Native snippets

If you’re working with React or Redux, this extension will save you a ton of time. It provides a set of pre-built snippets for commonly used React and Redux boilerplate code, allowing you to focus on building functionality instead of writing repetitive code.

Key features:

Provides snippets for React, Redux, and React Native development.
Speeds up coding by auto-generating component structures.
Snippets for React hooks, Redux actions, and reducers.
Supports JavaScript and TypeScript.
Highly customizable snippet configurations.

Install on VS Code

9- JavaScript (ES6) Code Snippets

JavaScript (ES6) Code Snippets is a productivity booster for anyone working with modern JavaScript. It offers pre-built snippets for common ES6+ syntax, allowing you to write less boilerplate and avoid common typos or mistakes.

Key features:

Provides snippets for ES6+ features like arrow functions, destructuring, and promises.
Simplifies complex JavaScript syntax.
Works for both JavaScript and TypeScript.
Supports commonly used patterns like import/export, classes, and modules.
Customizable snippet settings.

Install on VS Code

10- Wallaby.js

Wallaby.js is a powerful tool for running JavaScript tests in real-time. It provides instant feedback as you write your tests, showing which tests pass, fail, or need improvement right inside your code editor, drastically reducing your debugging time.

Key features:

Runs JavaScript and TypeScript tests in real-time.
Provides live feedback on test results within VS Code.
Displays code coverage for tested code.
Supports various testing frameworks like Jest and Mocha.
Helps debug by highlighting errors directly in your code.

Install on VS Code

11- Console Ninja

If you spend a lot of time debugging through console.log, Console Ninja is your best friend. This extension supercharges the native console by adding features like filtering, grouping, and better visualizations of log outputs, making debugging far less tedious.

Key features:

Enhances the functionality of console.log debugging.
Supports filtering, highlighting, and searching within console logs.
Auto-cleans logs after execution for a clean debugging experience.
Works with JavaScript, TypeScript, and Node.js.
Group and expand/collapse logs for better organization.

Install on VS Code

12- Auto Rename Tag

Auto Rename Tag is a small but incredibly useful tool that ensures when you rename an HTML tag, its closing tag is automatically updated too. It saves you from manually changing both tags and reduces the chances of markup errors.

Key features:

Automatically renames paired HTML/XML tags when you edit one.
Works across HTML, JSX, and XML files.
Supports nested tags for deeper renaming.
Lightweight and works seamlessly in the background.
Saves time when refactoring large HTML structures.

Install on VS Code

13- GitLens

GitLens supercharges your Git experience within VS Code by providing insights into your codebase’s history. From showing who last modified a line of code to revealing the history of commits for a specific file, GitLens gives you deep visibility into your project’s evolution.

Key features:

Shows who modified a line of code and when.
Highlights file and line commit history directly in the editor.
Git blame annotations for quick code ownership insights.
Visualizes Git graphs and timelines of changes.
Provides seamless integration with GitHub and GitLab.

Install on VS Code

Conclusion

By using these 13 powerful VS Code extensions, you can overcome the common challenges JavaScript developers face — like messy code, difficult debugging, and inefficient workflows.

Each of these tools brings something unique to the table, making you a more productive, efficient, and effective developer.

Whether you’re just starting out or you’re a seasoned JavaScript pro, these extensions will help you code smarter, not harder. Try them out, and watch your development process transform.

Introducing Andromo - Create Android Apps Without Coding 2025

Muhammad Furqan Ul Haq — Thu, 01 Apr 2021 13:54:32 +0000

Introducing Andromo - Create Android Apps Without Coding

Have you ever tried to learn how to develop an Android app?

If so, you might know that it requires knowledge of programming languages like Java or Kotlin.

Android development is surely a difficult and complex task. But, the Andromo app maker provides you a solution that makes the job a lot easier. It allows you to make an Android app without coding.

It’s a website, meaning that you don’t need to install Android Studio or any other software on your device.

In my opinion, Andromo is a great tool for those who find programming a bit difficult. It enables you to design an app by offering a range of ready-made app features.

For example, you can add the following features with just a click.

Photo Gallery / Wallpaper
Audio
Video
Maps
Surveys
Polls
Ads
etc.

And you know what, it’s even easier to customize the design according to your business requirements.

So, no matter whether you are a programmer or not. You can now design, monetize, and publish your app by performing simple steps.

There is a huge number of apps on Google play that are built on Andromo. You can build Games, Recipe apps, Educational apps, Business apps, and many others according to your requirements.

Today, I will explain to you the features and working of Andromo in detail.

So, let’s get started!

Features

Andromo has tons of features (aka Activities) that could help you design any kind of app. But, for the sake of this blog post, I’ll briefly introduce you to a few of them.

Google Maps

Andromo Map activity provides you the facility of displaying your location on the Map. Your user can easily get the navigation and direction of your business.

PDF

By using Andromo PDF activity you can easily insert a PDF file into your Android application. But, remember that this PDF document will open externally.

Youtube Video

You can display Youtube videos inside your Android app by using Youtube activity. Youtube Data API v3 is used in this activity.

Audio

You can create an audio player inside your Android application by using Audio activity.

Photo Gallery

You can upload a photo in your app and display it in the Gallery section by using the Photo Gallery activity. This Activity also allows you to set the size and layout of the photo.

How It Works

In this section, I’m going to explain to you the working model of the Andromo website and how you can make an Android application using it.

At first, you need to create an account on the Andromo.com website. It’s free and no credit card is required. Basically, they offer a completely free trial for two weeks. During this time you have access to all the activities.

I think 14 days are enough to explore this app builder and create a mobile app. After that, if you like this platform then I would highly recommend you to buy a subscription plan.

I myself bought its “PRO” plan that helps me design custom apps for my clients with ease.

Anyways, login to the Andromo.com website and then click on the “CREATE AN APP FOR FREE” button.

You have to pick the theme according to your requirement, and then give a name to your app. After that click on the “BUILD THIS APP” button and you are ready to go.

You will find the following tabs on your screen

When you click on any of these tabs they provide you different functionalities. The most important ones are Activities and Theme.

I will briefly discuss some of these functionalities to help you get familiar with Andromo.

Activities

This tab decides the working of the app. I picked a theme for the Fashion app and the following activities are already included in our app.

We can add or delete activities according to our requirements. There are many other activities available including PDF documents, HTML Archive, Photo Gallery, etc.

You can also change the position of the activity by drag and drop according to your requirements.

When you click on any Activity the following page appears on the website.

There are five tabs inside every activity including Settings, Content, Style, Images, and Toolbar. Now, I will discuss all of them one by one.

Settings

You can insert the Name, Subtitle, and Description of the Activity in this section.

Content

You can place content inside your app according to your activity. Content may include the website’s URL or pictures depending on the activity you’ve selected.

Style

You can change the style of the theme to Global or you can customize it according to your choice.

Images

There is an option of Feature Image and Custom Image and you can also pick Grid Layout or Drawer Icon of your choice.

Toolbar

In the Toolbar, you can customize the design of your app according to your needs.

Theme

In this option, you are allowed to change the theme of your app. When you select the Theme icon, the following page appears.

There are seven tabs under the Theme icon including Startup, Navigation, Settings, Layout, Style, Images, and Toolbar.

Startup

Startup is the first option that controls the initial screen at the launching of your app. You can add any activity in the Dashboard that is selected in the Activity tab.

Navigation

In the Navigation part, you can Enable or Disable the navigation drawer and you also have to decide whether the drawer remains open or close at the start of App.

You can add Title, Subtitle, and Header Images in the Dashboard.

Settings

In the setting tab, you can change your Dashboard name and also show the social sharing buttons.

Layout

You can select the layout of your choice. You have the option of selecting it by appearance or you can design your layout in the form of a Grid, card, or list.

Style

You have to select the Style mode custom or global. In custom, you can change color according to your choice. You can also customize Bodystyle which includes Window Color, Background Color, and Text Color.

Images

In this section, you can upload Background Image and Feature Image. You can even select the layout of your image and change the Drawer icons.

Toolbar

In the toolbar, you have to select custom or global mode. In the custom mode, you can change the color, icon, and Background according to your choice.

Global Style

On the Global style page, you have three tabs Color settings, Image settings, and Toolbar settings. When you click the Global Style menu, the following page appears.

These are the simple options of setting the color, images, and toolbar. Basically, you can customize the look and feel of your app by using these options.

Settings

When you click on the Settings tab following page appears.

Here, you can provide information related to your app like its name, icon, version, etc.

Monetization

Most of us usually want to earn money by creating Android apps. So, this page is going to be the most important one.

When you click on the Monetization tab following page appears.

Through this Monetization tab, you can enable ads in your app and you can use any of the above networks that are shown in the image.

Services

In the services tab, you can add Firebase Analytics, OneSignal, and Intercom. You can view statistics by using Firebase Analytics.

Build

After completing the app you have to build it. This is the last step before you can run it on your Android devices.

Conclusion

In my opinion, Andromo is one of the best app makers for Android. You can create a mobile app for your business even if you don’t have any coding experience.

It is interesting to note that 1.1 million people are actively using Andromo to quickly build their Android apps.

So, what are you waiting for? Try this free android creator now!

16 Best Kubernetes Books for Beginners 2025

Muhammad Furqan Ul Haq — Thu, 11 Mar 2021 10:45:42 +0000

Kubernetes is a container orchestration platform. Now, you might be wondering what does that really mean. So today, I've compiled a list of the best Kubernetes books that might help you understand the core concepts of Kubernetes in detail.

Some books will walk you through the fundamental concepts of containers, orchestration, Kubernetes, continuous deployment, cloud-native, etc. in a step-by-step manner. Whereas, I've also added a few books for people who already have some familiarity with Kubernetes and are looking for best practices.

No matter whether you are a complete newbie to the world of Kubernetes or are already working as a DevOps engineer, this guide will help you select the Kubernetes book that is most suitable for you according to your requirements.

Best Kubernetes Books

It's time to dive into the most awaited list of best Kubernetes books. I've carefully compiled it to help you give a variety to choose from. All of these books are great and informational, they all have something unique to offer.

1- Kubernetes First Steps

As its name suggests, Kubernetes First Steps is a combination of an eBook, videos, and some extra stuff that is designed for absolute beginners. At first, it helps you get started with Docker and Kubernetes. Later on, you will be introduced to more complex topics in an easy-to-understand manner.

What will you learn?

How to package applications as Docker containers.
Kubernetes fundamentals.
How to scale applications.
Kubernetes self-healing properties.
How to troubleshoot failed deployments.
Use Kubernetes locally with minikube.
In-depth understanding of YAML.
Start using kubectl, a command-line tool for Kubernetes.

If you can just afford to buy one Kubernetes book then I would highly recommend you to go for it. It's because I personally like their additional stuff like videos, code examples, support from the instructors on Slack, Kubernetes cheatsheet, and interactive exercises on minikube.

All of this combined in a single course will definitely enhance your learning experience.

Get It Now

2- The Kubernetes Book

The Kubernetes Book helps you become proficient at Kubernetes. It clearly explains each and every concept of Kubernetes. Here, you'll learn about:

Kubernetes architecture.
Deploy, scale, perform rolling updates, and even self-heal applications.
Real-world security and threat-modeling in Kubernetes.

According to the author Nigel Poulton, he will update this book at least once a year to keep up with the latest Kubernetes trends.

Get It Now

3- Kubernetes: Up and Running: Dive into the Future of Infrastructure 2nd Edition

This book is actually written by people who developed Kubernetes at Google. So, it's a great opportunity for you to learn Kubernetes from the ones who know each and everything about it.

It explains the use of tools and APIs to build scalable distributed systems automatically.

No matter whether you want to use Kubernetes for machine learning applications, online services, or even smaller Raspberry Pi computers, this book is right for you.

Here the authors will practically teach you how Kubernetes can be used to create and deploy real-world applications.

Get It Now

4- Kubernetes App Development

Are you familiar with programming languages like JavaScript, Python, Java, or PHP?

If so, this book might be more suitable for you. It's because the author has specifically designed this book for software developers who want to pass the CKAD (Certified Kubernetes Application Developer) certification exam.

At first, you will learn how to install Kubernetes and Docker. Then, you'll be introduced to Kubernetes CLI aka kubectl. At this point, you'll learn many commands to interact with Kubernetes.

The author will also guide you about the complete architecture of Kubernetes. It is very important because Kubernetes is a new technology and has introduced many concepts that were never seen before.

For example, you'll learn about:

Kubernetes pod.
Patterns (such as Sidecar pattern, Adapter pattern, and Ambassador pattern).
Rolling updates and rolling back.
Service Accounts.
Monitoring, Debugging, and Logging errors.

Finally, a complete guide to passing the CKAD exam will be provided. It contains information regarding the exam style, sample questions, and some actionable advice from the author.

Get It Now

5- Understanding Kubernetes in a visual way

This book is a bit different from the others mentioned here. Aurélie Vache (the author) has created a collection of sketch notes about Kubernetes which might help you learn this amazing technology in a visual way.

These sketch notes cover the basics as well as many advanced topics like Kubernetes components, Horizontal PodAutoScaler, Node Affinity, Network Policies, etc.

It could be the right choice for you if you prefer learning through interesting notes instead of a well-formatted book.

Get It Now

6- Learn CKS Scenarios

The Certified Kubernetes Security Specialist (CKS) program is an exam that determines the skill set of Kubernetes engineers. It helps the current or potential employers to see whether you have the right skill set to build, deploy, and secure container-based applications through best practices.

This book will help you prepare for the CKS Certification exam. It contains many scenarios and practice questions that will definitely polish your Kubernetes skills.

I only recommend this book if you already have a good understanding of Kubernetes and are looking for some quick guide before appearing in the CKS exam.

Get It Now

7- Kubernetes in Action, Second Edition

In the beginning, this book will help you understand what is Kubernetes and how it can deploy container-based distributed applications with ease. After that, the author will gradually introduce you to new features.

After reading this book, you'll be able to monitor, scale, and tune applications using Kubernetes.

Get It Now

8- Cloud Native DevOps with Kubernetes: Building, Deploying, and Scaling Modern Applications in the Cloud

Kubernetes is no doubt an excellent invention for the cloud-native world. The authors of this book John Arundel and Justin Domingus are basically cloud experts. They will teach you the complete ins and outs of the Kubernetes ecosystem.

Here, you'll learn to build a cloud-native application with its supporting infrastructure. You will also be introduced to a continuous deployment pipeline which is very important for production applications.

Get It Now

9- Kubernetes Best Practices: Blueprints for Building Successful Applications on Kubernetes

I would say that this book is not for absolute beginners. But, a great option for those who want to learn the current best practices to use Kubernetes in real-world applications.

You will learn different patterns for monitoring and securing your systems. Additionally, I recommend this book if you want to understand Kubernetes networking policies.

Get It Now

10- Managing Kubernetes: Operating Kubernetes Clusters in the Real World

This book is designed for people who prefer tons of practical examples as compared to just theory. It is written by one of Kubernetes co-founder Brendan Burns. I think he can better teach you how Kubernetes works internally due to his massive involvement in this open-source project.

The author will demonstrate ways to maintain and enhance the cluster according to your requirements.

Get It Now

11- Introduction to DevOps with Kubernetes: Build scalable cloud-native applications using DevOps patterns created with Kubernetes

Do you want to start a career in DevOps?

This book will walk you through the concepts of DevOps using Kubernetes. Both of these are key to ensure the high performance of IT infrastructure.

It reveals the operational challenges we face in real-world apps and gives solutions to overcome them.

After reading this book, you can easily monitor an app by setting up a pod failure alert on Prometheus. It also covers topics like kubectl commands and how to configure Alertmanager to send alerts to the Slack channel.

Get It Now

12- Kubernetes Patterns: Reusable Elements for Designing Cloud-Native Applications

Microservices and Containers have significantly changed the way we build and deploy software applications. In this book, Bilgin Ibryam and Roland Huß who works at Red Hat will teach you about common practices for designing and implementing cloud-native apps on Kubernetes.

They also provide you some reusable elements, patterns, and principles that will boost your productivity while building real-world applications.

You must have some basic understanding of Kubernetes before buying this book. It is more suitable for those looking for common cloud-native patterns.

This book focuses on the following pattern categories.

Foundational patterns
Behavioral patterns
Structural patterns
Configuration patterns
Advanced patterns

Get It Now

13- Kubernetes Cookbook: Building Cloud Native Applications

This book takes a practical approach to help you automate deployments and manage different operations of application containers across clusters of hosts.

It is based on a problem-solution-discussion format. Meaning that this book has detailed answers for many common issues you might face while working with Kubernetes.

Here, you will learn the following topics.

How to install Kubernetes?
Kubernetes API and API groups
Application primitives
Monitor the state of an app. What to do if something goes wrong.
Quickly troubleshoot common issues.

Get It Now

14- Kubernetes Operators: Automating the Container Orchestration Platform

In Kubernetes, operators are used for packaging, deploying, and managing applications. They can automate complex stateful applications by adding application-specific operational knowledge to a Kubernetes cluster.

Operators are frequently used for application upgrades, automatically react to failures, and streamline repetitive tasks like backups.

This book revolves around Kubernetes Operators. Here, the authors will teach you how to create operators using Operator Framework and SDK.

Get It Now

15- Mastering Kubernetes: Level up your container orchestration skills with Kubernetes to build, run, secure, and observe large-scale distributed apps, 3rd Edition

Do you want to become an intermediate or advanced level Kubernetes professional?

Then, this book will be the right choice for you.

It not only introduces you to Kubernetes fundamentals but also helps you explore many advanced topics like:

Service Meshes
Serverless Computing
Cluster Federation
Autoscaling

Get It Now

16- Programming Kubernetes: Developing Cloud-Native Applications

This book is written by Michael Hausenblas (AWS developer advocate) and Stefan Schimanski (Red Hat principal software engineer).

They help you develop Kubernetes-native applications. These apps can directly interact with the API server to find out or simply update the state of resources.

Here, you'll learn about the client-go API library and custom resources.

Before you buy this Kubernetes book, you must have some know-how about Go programming language, Git, and package management.

Get It Now

Conclusion

You just saw the collection of best Kubernetes books. Now, I would like to mention some facts about its market share. It will definitely motivate you to learn this amazing technology.

According to a report published by Cloud Native Computing Foundation (CNCF), containers used in the production environment have seen a massive increase of 300% since 2016. Also, the usage of Kubernetes in production has increased to 83%, up from 78% last year.

So, the market share of Kubernetes is increasing continuously. In turn, it creates new job opportunities for DevOps engineers.

It is interesting to note that Kubernetes is mostly used by enterprise-level companies like Google, VMware, Deloitte, etc. So, you can expect a salary anywhere from $72K to $146K per year.

In my opinion, all of these Kubernetes books mentioned here are worth buying. They offer great value in return for the amount spent.

Nowadays, I'm reading Kubernetes First Steps. And so far I found it a very interesting and informational book. At the moment, I'm waiting for its video lectures which will be available soon (in the 2nd week of March 2021).

Disclosure: I only recommend products I would use myself and all opinions expressed here are my own. This post contains affiliate links. If you use these links to buy something then I may earn a small commission.

5 Best Places to Find DevOps Jobs in 2025

Muhammad Furqan Ul Haq — Thu, 11 Feb 2021 04:02:38 +0000

So, you've learned DevOps and are looking for a job? Or you might just be exploring the career opportunities for DevOps engineers before getting into this field.

Today, I will guide you where to search for DevOps job offers (remote + office) and how much the employers are willing to pay.

So, stick to it, as this article might help you get hired as a high paying DevOps engineer.

1- Stack Overflow

Stack Overflow is a highly active question/answer community of software developers and engineers. Nowadays, they also have a job board to connect developers with potential employers.

Many highly-reputed companies post their jobs here to find talented DevOps engineers. So, if you have the required skills then must give it a try.

There are many job listings where you can directly apply using your Stack Overflow account. Whereas, some jobs will redirect you to the company’s website to apply.

In my opinion, having a great Stack Overflow profile increases your chances to get hired. It’s because you’ve already proved your expertise by answering genuine questions.

Estimated Salary Range (per year): $70k - $155k (US Dollars)

2- Toptal

Toptal is considered a freelancing platform for only the best ones. You’ve to fulfill their strict requirements in order to get considered.

They walk you through an extensive test to analyze your skills. Toptal proudly mentions that they only select the Top 3% of DevOps engineers.

But, a point to be noted is that they offer way more salary packages than other platforms. It’s because most tech-giants like Airbnb, Shopify, Duolingo, etc. trust Toptal and prefer to hire the best talent from here.

Estimated Salary Range (per year): $100k - $160k (US Dollars)

3- Kube Careers

As its name suggests, Kube Careers is specifically designed for people having experience in Kubernetes.

It is relatively a new platform as compared to the rest of the job boards mentioned here. But, the unique thing about Kube Careers is that it brings Hand-picked Kubernetes jobs for you.

Each job clearly mentions whether it is office-based or remote, its salary range, and complete detail of the client’s requirements.

Simply click the “Learn more” button given near each job listing to open the job description. It will also reveal the “Apply” button through which you can apply for the given position.

Estimated Salary Range (per year): $48k - $200k (US Dollars)

4- LinkedIn

You might have heard about LinkedIn before, isn’t it?

Basically, it enables professionals to connect with each other. It has a dedicated section where you can search for jobs related to DevOps or any relevant field.

Here, you’ll frequently see job offers from big international businesses like GitHub, Amazon, Apple, Bank of America, Comcast, Zoom, Verizon, etc. In turn, you have a great opportunity to learn the best practices by joining one of them.

Estimated Salary Range (per year): $40k - $145k (US Dollars)

5- Indeed

Since November 2004, Indeed has proven to be the best and largest job listings portal. Every day there are tons of jobs posted regarding DevOps.

You just have to find the best fit according to your skills and apply!

If you are interested in a career opportunity for a specific DevOps subskill like AWS, Azure, Kubernetes, etc. then Indeed might be very helpful for you.

Just type the desired skill in the search box and hit “Enter”. It will bring all the job listings available for that specific skill. You can easily sort the results either by relevance or date.

Estimated Salary Range (per year): $50k - $180k (US Dollars)

How I Hacked Cookie Clicker Game with Inspect Element 2025

Muhammad Furqan Ul Haq — Tue, 02 Feb 2021 17:58:13 +0000

How I Hacked Cookie Clicker Game with Inspect Element

Cookie Clicker is a very popular browser-based game. It revolves around producing cookies for the world. This game gets more interesting over time which keeps the player engaged.

Basically, there are a few ways to get cookies. These cookies can then be used to create new buildings or purchase upgrades. At first, I’m going to mention a few genuine ways to earn cookies.

By clicking the big “Cookie” shown on the user interface.
By creating new buildings. There are different types of buildings and each type can only produce a specified number of cookies per second. Initially, all buildings are locked, but they will get unlocked over time as the game progresses.
By enhancing the efficiency of your cookie store through upgrades.

I would like to mention here that in the past I wrote a very popular article about the best open-source HTML Games. I think the source code of these games will help you improve your game development/hacking skills. So, please do have a look.

Now, here comes the actual hacking stuff you’re waiting for.

Getting Started

As I mentioned earlier, this is a browser-based game. Meaning that it is developed using HTML5, CSS3, and JavaScript. All of these are client-side languages that can be modified through programs like Chrome DevTools.

Let’s open this game in Google Chrome.

Now to open Chrome DevTools, right-click anywhere on the game and select “Inspect” from the context menu. For now, we will only be focusing on its functionality that is written in JavaScript. So, open the “Console” tab.

Another way is to press Ctrl+Shift+J on Windows or ⌘+⌥Option+J on Mac.

You’ve to execute the below-mentioned codes here.

It’s time to hack different features of this Cookie Clicker game.

Hack Cookies

As everything in this game depends on cookies, so I hacked them!

Basically, after analyzing its source code, I found that the functionality of this game is written inside a file called “main.js”.

This file initializes an object Game through which we can easily access different features of Cookie Clicker. For example, we can modify the number of cookies we have using the Game.cookies property.

Let’s do that now!

Simply copy the below code and paste it inside the “Console” tab of Chrome DevTools. Press “Enter” to execute it.

Game.cookies = 57000

Once executed, you will get 57000 cookies absolutely FREE. You can add any number here.

For example, the below code will give you “Infinity Cookies”.

Game.cookies = 999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999

Keep Getting Cookies

This game also has the functionality of earning cookies per second. Game.cookiesPs property will give us a specified number of cookies for one second.

So, why not wrap it inside a setInterval of one second to continuously get more and more cookies?

Here’s the code snippet to do so.

setInterval(function(){
    Game.cookiesPs = 50
}, 1000);

This code will add 50 cookies to your account every second.

Unlock All Upgrades and Purchase Them

Finally, the developers of Cookie Clicker have also provided a cheat to get all upgrades for free.

Basically, the below method will give you all upgrades as well as unlocks all types of buildings. Additionally, you will get 1.000 nonillion cookies.

Game.RuinTheFun();

Now you might be wondering can we hack other browser-based games too?

And the answer is "Yes, definitely!"

You can hack any game no matter whether it is browser-based, Android, or even PC.

I would like to mention that I learned game hacking from guidedhacking.com. It is the best community forum that helps us understand all the concepts of game hacking in a step-by-step manner.

Spend some time there!

You'll find game hacking tutorials and guides right from their main menu. If you find something difficult then you can always ask questions from fellow hackers.

At last, I would say that game hacking is not very difficult. It just depends on how much time you can dedicate to learn it.

Shopify vs WooCommerce - The Best Choice for Dropshipping 2025

Muhammad Furqan Ul Haq — Fri, 06 Nov 2020 08:25:06 +0000

Traditionally, starting an online eCommerce store requires some investment. The major portion of this money is used to purchase the inventory. Meaning that you need to have some products in stock before selling them.

But, dropshipping has completely changed the way we do business online. It introduces the concept of selling someone else products in our store.

To get started, we have two popular options. Whether set up a Shopify store or go with WordPress plugin WooCommerce.

Overview of Shopify and WooCommerce

Shopify is an online service that enables you to launch an eCommerce business with ease. Basically, they manage the technical stuff for you like hosting, security, updates, etc.

Whereas, WooCommerce is a WordPress plugin through which you can create and customize an eCommerce store according to your needs. It gives you much more flexibility than Shopify as you’re in complete control.

How Dropshipping Works?

In dropshipping, you need to source products from online Suppliers and Vendors. Basically, when someone places an order on your website/app then the supplier is automatically informed about it. They receive the order details as well as where to ship them.

Once the order is ready, the supplier ships it to the buyer. Our job is just to bring more customers and the supplier will handle their orders.

Here, we earn money through margins. Meaning that if a supplier is selling a product for $100 and we add that product on our website at $125, then we will earn $25 for each sale.

Dropshipping with Shopify

Shopify is a software to set up an online shop. It has tons of features that make it stand out from other eCommerce platforms.

For example, it has features like:

Readymade themes that are specifically designed for online stores.
Integration with 100 payment gateways.
Launch your shop in 50+ languages.
Out of the box support for Dropshipping.
Quickly integrate with social media platforms. For example, you can sell on Facebook from Shopify.
Intuitive inventory management functionality.
Support for Video & 3D assets.
Subscriptions.
Fast speed web hosting including SSL certificate.
Product reports and analytics.
Support for Augmented reality (AR)

It is a paid service that works on a subscription model. They have three packages to fulfill the needs of small, medium, and large eCommerce businesses.

Basic Shopify – Starting at $29 per month
Shopify – Starting at $79 per month
Advanced Shopify – Starting at $299 per month

Dropshipping with WooCommerce

On the other hand, WooCommerce is an open-source WordPress plugin that allows you to host an online shop yourself.

There are many hosting companies out there, but using the best one is very important for the overall success of the eCommerce business. Here’s a comparison between the best WordPress hosting to help you make an informed decision.

The major reason for using WooCommerce is that Shopify is very costly. They even charge a fee per sale. But, while using WooCommerce we don’t have to give any subscription fee.

As we have the complete source code of WooCommerce so we can easily customize it for any special use case.