Chatbox is an open-source desktop client that provides a unified interface for ChatGPT, Claude, and other LLMs across Windows, Mac, and Linux.

- Local-first architecture with data stored on your machine
- Supports multiple AI models through a single application
- GPLv3 licensed community edition with regular code syncs from the pro version
- Available as native desktop apps for all major operating systems

Explore it here:
https://www.opensourceprojects.dev/post/chatbox

As a SOFTWARE ENGINEER, are you scared of BACKEND DEVELOPMENT?

I promise you'll enjoy it after going through this thread:

My entire AI stack is now Chinese 🇨🇳

87% cheaper. same revenue

swaps by task:

1. reasoning / backend brain
Opus 4.8 → Kimi K2.7
benchmark gap: ~8% · price: ~11x cheaper

2. code generation
GPT-5.5 → Qwen 3.7 Max
benchmark gap: ~18% · price: ~7x cheaper

3. agent loops + tool calling
Sonnet 4.7 → GLM 5.2
benchmark gap: ~3% · price: ~5x cheaper on input

4. cheap volume / bulk processing
GPT-5.5 mini → MiMo V2.5
benchmark gap: ~6% · price: ~12x cheaper

5. image generation
GPT-Image-2 → Wan 2.5
benchmark gap: ~5% · price: ~8x cheaper

6. video generation
Sora 2 → Kling 3.0
benchmark gap: roughly equal · price: ~6x cheaper

[ result after 30 days: ]

operating costs dropped 87%, output quality dropped 4% on average, revenue unchanged

the most important that these models will be not banned in a month and i can run them locally

nobody will steal my data and i can learn them as i need

full article drops tomorrow with:
> exact routing logic per task type
> the 2 cases where I still pay for American
> the migration playbook anyone can copy in a weekend

VERY IMPORTANT to get migrated now, while it's not too late

☀️ The wind made the whole frame come alive!

Made a set of “Summer Sky Low-Angle Portraits” with ChatGPT-Image2.

Low angle, blue sky, full-body framing.
Hair flying, skirt moving, real skin tones.

It feels like the exact second when the wind gets stronger and the camera catches it.

Prompt below 👇

Stealth Browser MCP features 97 tools to bypass Cloudflare and other antibot systems.

https://github.com/vibheksoni/stealth-browser-mcp https://x.com/tom_doerr/status/2071309381455519803/photo/1

Karpathy's Agentic Engineering finally has proper tooling!

(built by Google)

Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight.

The problem has been that practicing this still requires a different tool for every phase:

- editor for code
- a terminal for scaffolding
- a browser for testing
- a cloud console for deployment
- and a separate framework for evals.

Every transition is a context switch.

The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI.

It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents.

One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language.

I tested this end-to-end by building a RAG agent from scratch using Claude Code.

It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard.

Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it.

The video below shows this in action, and I worked with the Google Cloud team to put this together.

Agents CLI GitHub repo → https://github.com/google/agents-cli?utm_source=fnf&utm_medium=x&utm_campaign=google-cloud-june&utm_term=akshay-pachaar&utm_content=agents-cli-github

(don't forget to star it ⭐ )

I wrote up the full build covering all six steps from install to enterprise registration.

It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end.

Read it below.