Friday, June 27, 2025

Gemini vs Claude vs Codex CLI – Full Comparison

 AI-powered terminal assistants are reshaping development workflows. Top players like Gemini CLI, Claude Code, and OpenAI’s Codex CLI offer powerful natural-language interfaces for coding, debugging, and automation. Let’s explore their strengths, weaknesses, and how they compare across key criteria.

1. Open Source & Licensing

  • Gemini CLI: Fully open-source under Apache 2.0, including system prompts and model-context protocol (MCP) extensions.

  • Codex CLI: Also Apache-licensed, supports multiple OpenAI models via CLI .

  • Claude Code: Proprietary—closed beta with limited access and no source code release .

Verdict: Gemini CLI and Codex CLI lead in openness and community potential.

2. Free Tier & Cost

  • Gemini CLI: Generous free tier—60 requests/min, 1,000 daily requests, 1M-token context window, completely free to start.

  • Codex CLI: Requires an OpenAI API key; offers limited free tokens, scaling costs with usage .

  • Claude Code: Expensive—pay-per-token pricing makes sustained use costly (~$200+/mo).

Verdict: Gemini CLI offers the best free access and cost-effective capabilities.

3. Model Power & Context Window

  • Gemini CLI: Access to Gemini 2.5 Pro with a 1 million token window—unmatched for large codebases or documents.

  • Codex CLI: Supports large context models (~192k tokens), sufficient for most tasks.

  • Claude Code: Context capacity similar to Codex, with strong reasoning but slides into high usage costs.

Verdict: Gemini CLI stands out for handling large-context workflows.

4. User Experience & Integration

  • Gemini CLI: Simple CLI interface; integrates MCP, supports Windows/Linux/macOS .

  • Codex CLI: Three interactive modes—Suggest, AutoEdit, FullAuto—with built-in security sandboxes .

  • Claude Code: Offers polished UX with IDE integrations (VSCode, Cursor) and built-in memory session features.

Verdict: Claude Code provides the smoothest UX, followed closely by Codex CLI, with Gemini CLI being functional yet less polished.

5. Reliability & Multistep Task Performance

  • Gemini CLI: Occasionally falters with agent orchestration; some users report limited sub-agent support.

  • Codex CLI: Stable and gateway-safe approach using sandboxed execution—ideal for automated tasks .

Claude Code: Performs well in task sequencing and memory; strong reasoning reduces prompt overhead.

Verdict: Claude Code excels in reliability; Codex CLI prioritizes safety; Gemini CLI needs more refinement.

6. Extensibility & Ecosystem

  • Gemini CLI: Very extensible via MCP—supports custom toolchains, integrations, and community-driven development.

  • Codex CLI: Flexible provider switching and config-based workflows .

  • Claude Code: Limited public extensibility, though its closed ecosystem offers deep features .

Verdict: Gemini CLI offers the most open extensibility; Codex CLI is configurable; Claude Code is limited.

7. Community Feedback

Reddit discussions highlight:

"Gemini CLI looks polished, but Claude Code is still superior for agentic workflows."
"Gemini told me it cannot spawn subagents—it runs tools in parallel only"

These reveal Gemini is promising yet immature in advanced use cases, while Claude remains robust.

Overall Comparison :





Which Tool Should You Use?

  • Choose Gemini CLI if you want free, powerful, and open‑source AI tooling with massive context support—and you don't mind tweaking things.

  • Choose Codex CLI if you need secure automation, multi‑provider flexibility, and a more mature interface.

  • Choose Claude Code if you're building complex agentic pipelines and are prepared to pay for premium UX and reliability. 


🔗 References & Sources (for credibility and linking):

1. Gemini CLI by Google DeepMind

2. Claude Code by Anthropic

3. OpenAI Codex CLI

4. Relevant Comparisons or News

Written Friday, June 27, 2025 by Sangram Kakade

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Monday, June 16, 2025

Web Scraping is the Secret Weapon Behind Powerful RAG Systems



Everyone’s talking about Retrieval-Augmented Generation (RAG) a hybrid AI architecture where a retriever brings in real-world knowledge to ground the responses of a generative model like GPT.

But here’s the part that rarely gets attention:

The quality of your RAG system is only as good as the data you feed into the retriever.

That’s where web scraping becomes essential.

How Web Scraping Supercharges RAG:

1. Real-Time, Domain-Specific Knowledge
Traditional knowledge bases are often outdated or narrow in scope.
Web scraping helps you build dynamic, up-to-date corpora from sources like:
 • News articles
 • Research blogs
 • Product documentation
 • Developer forums (Stack Overflow, Reddit, etc.)
 • Government portals
This ensures your retriever always has fresh, relevant context to ground its outputs.

2. Long-Tail Content Coverage
APIs and curated datasets miss the long tail — the obscure but important info:
 • Niche product comparisons
 • Legal edge cases
 • User-generated troubleshooting solutions
Scraping fills in the gaps, allowing your RAG to perform well even in specialized domains.

3. Verticalized Intelligence
Want to build RAG systems for finance, health, aviation, legal, or tech support?
Scraping lets you construct vertical-specific knowledge bases directly from trusted industry sources. This boosts:
 • Precision in retrieval
 • Relevance in generation
 • Trustworthiness of outputs

4. Grounding Without Hallucination
By scraping authoritative sources and indexing them into vector stores (e.g., FAISS, Weaviate, Pinecone), your RAG system retrieves grounded facts, reducing hallucination significantly — especially for high-stakes use cases.

5. Continuous Learning & Auto-Reindexing
With scheduled or event-driven scrapers, your RAG system can automatically refresh its index, making it adaptive and self-updating — without retraining the model.

⸻

Bottom Line:
RAG is only as powerful as your retrieval pipeline.
And web scraping gives you the power to build and maintain that pipeline — across time, domains, and languages.

So if you’re building:
 • Domain-specific assistants
 • Support chatbots
 • Internal copilots
 • Competitive intelligence tools

…web scraping isn’t just useful — it’s critical.

Written Monday, June 16, 2025 by Sangram Kakade

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Thursday, March 8, 2018

How to Install Broadcom Wi-Fi Drivers on Fedora (2025 Guide)

If you're using a Fedora system and experiencing issues with Broadcom Wi-Fi, you're not alone. Fedora does not include proprietary drivers like Broadcom's by default. However, you can resolve this by installing the broadcom-wl driver through the RPM Fusion repositories.

Follow the steps below to get your wireless working.

Step 1: Enable RPM Fusion Repositories

To access the broadcom-wl driver, you need to enable both the free and non-free RPM Fusion repositories. You can visit the official configuration page:

https://rpmfusion.org/Configuration

Alternatively, install them directly via the terminal:

bash


# Enable the Free repository
sudo dnf install https://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm

# Enable the Non-Free repository
sudo dnf install https://download1.rpmfusion.org/nonfree/fedora/rpmfusion-nonfree-release-$(rpm -E %fedora).noarch.rpm

Step 2: Update Your System

It is recommended to update your system before installing new drivers:

bash

sudo dnf update
sudo dnf upgrade

Step 3: Install the Broadcom wl Driver

Once the repositories are enabled, install the driver package:

bash

sudo dnf install broadcom-wl

Step 4: Handle Missing Kernel Modules (If Required)

If you encounter errors related to modprobe or missing kernel modules, it's likely that the kernel-devel package is not installed. Use the following commands to resolve it:

bash

sudo dnf install -y kernel-devel

sudo akmods --force --kernel $(uname -r) --akmod wl

Step 5: Load the Driver

Once everything is installed, load the Broadcom driver manually:

bash

sudo modprobe wl

If the wireless interface still doesn't activate, try rebooting the system.

Optional: Load wl Module at Boot

To ensure that the wl module loads automatically at boot time, create a configuration file:

bash

echo "wl" | sudo tee /etc/modules-load.d/wl.conf

Conclusion

Installing Broadcom Wi-Fi drivers on Fedora requires a few extra steps, but once configured correctly, it offers stable wireless performance. Using RPM Fusion ensures you're accessing trusted packages, and with a little terminal work, your system will be fully functional.

If you're managing multiple systems or setting up Fedora regularly, consider scripting these steps for faster deployment.

Written Thursday, March 08, 2018 by Sangram Kakade

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