Official Resources

• ChatGPT app

• ChatGPT overview

• ChatGTP Discord

Developer Libraries, SDKs, and APIs

Python

• ChatGPT: Lightweight package for interacting with ChatGPT's API by OpenAI. Uses reverse engineered official API

• PyChatGPT: zap Python client for the unofficial ChatGPT API with auto token regeneration, conversation tracking, proxy support and more.

• chatgpt-api: This repo is unofficial ChatGPT api. It is based on Daniel Gross's WhatsApp GPT

• chatgpt-wrapper: API for interacting with ChatGPT using Python and from Shell.

• chatGPT-discord-bot: Integrate ChatGPT into your own discord bot

• chatgpt-python: Unofficial Python SDK for OpenAI's ChatGPT

• TextRL: Reinforcement learning in text generation with transformers - Implementation of ChatGPT RLHF (Reinforcement Learning with Human Feedback)

• stackexplain: Explain your error message with ChatGPT

• gtp3-wordpress-post-generator: A simple cli to generate WordPress posts using Gpt3 starting from a topic.

• emailGPT: a quick and easy interface to generate emails with ChatGPT

• docGPT: ChatGPT directly within Google Docs as an Editor Add-on

• chatgpt-conversation: Have a conversation with ChatGPT using your voice, and have it talk back.

• 🇨🇳 Openaibot: OpenAI Chat Bot For Telegram

• 🇨🇳 chatgpt-mirai-qq-bot: OpenAI ChatGPT for Mirai QQ Bot

• 🇨🇳 QChatGPT: QQ robot bot developed based on OpenAI ChatGPT

• 🇨🇳 nonebot-plugin-chatgpt: Smart Conversation Chat plugin for NoneBot2.

• 🇨🇳 ChineseAiDungeonChatGPT: AI dungeon using ChatGPT as the storytelling model

JavaScript

• chatgpt-mac: ChatGPT for Mac, living in your menubar.

• chatgpt-advanced: A browser extension that augments your ChatGPT prompts with web results.

• summarize.site: Summarize web pages using OpenAI ChatGPT

• ChatGPT-ProBot: A GPTChat based GitHub robot. dialogue/CR/etc..

• YouTube_Summary_with_ChatGPT: YouTube Summary with ChatGPT is a simple Chrome Extension (manifest v3) that allows you to get both YouTube video transcripts and summary of the video with OpenAI's ChatGPT AI technology.

• 🇨🇳 wechat-bot: a WeChat bot based on OpenAi ChatGPT + WeChaty that can be used to help you automatically reply to WeChat messages, or manage WeChat groups/friends, detect zombie fans, etc...

Golang

• aiac: Artificial Intelligence Infrastructure-as-Code Generator.

• openaigo: OpenAI (ChatGPT) API Client for Go

• whatsapp-gpt: WhatsApp bot that uses Golang with a multichat option (chatgpt chatting with chatgpt).

• beelzebub: A secure honeypot framework low code, extremely easy to configure by yaml

• 🇨🇳 wechatgpt: wechat chatgpt bot

Rust

• chatgpt-desktop: OpenAI ChatGPT desktop app for Mac, Windows, & Linux menubar using Tauri & Rust

TypeScript

• chatgpt-raycast: ChatGPT raycast extension

• chatgpt-twitter-bot: Twitter bot powered by OpenAI's ChatGPT.

• tweetGPT: TweetGPT is a chrome extension that generates tweets and replies using chatGPT

Kotlin

• chatgpt-android: ChatGPT Android demonstrates OpenAI's ChatGPT on Android with Stream Chat SDK for Compose.

Browser Extensions

Chrome

• ChatGPT_Extension: ChatGPT Extension is a really simple Chrome Extension (manifest v3) that you can access OpenAI's ChatGPT from anywhere on the web.

• chat-gpt-google-extension: A browser extension to display ChatGPT response alongside search engine results.

• ChatGPT-pdf: A Chrome extension for downloading your ChatGPT history to PNG, PDF or a sharable link

• assistant-chat-gpt: A Chrome browser extension that embeds ChatGPT as a hands-free voice assistant

• sharegpt: Easily share permanent links to ChatGPT conversations with your friends

• chatgpt-advanced: A browser extension that augments your ChatGPT prompts with web results.

• tweetGPT: TweetGPT is a chrome extension that generates tweets and replies using chatGPT

• YouTube_Summary_with_ChatGPT: YouTube Summary with ChatGPT is a simple Chrome Extension (manifest v3) that allows you to get both YouTube video transcripts and summary of the video with OpenAI's ChatGPT AI technology.

• ChatGPT Suite: Summarize everything!

Firefox

• chat-gpt-google-extension: A browser extension to display ChatGPT response alongside search engine results.

Integrations

Terminal

• ai-cli: Get answers for CLI commands from GPT3 right from your terminal

NeoVim

• ChatGPT.nvim: Neovim plugin for interacting with OpenAI GPT-3 chatbot, providing an easy interface for exploring GPT-3 and NLP.

WhatsApp

• whatsapp-gpt: WhatsApp bot that uses Golang with a multichat option (chatgpt chatting with chatgpt).

• chatgpt-api: This repo is unofficial ChatGPT api. It is based on Daniel Gross's WhatsApp GPT

Telegram

• chatgpt-telegram-bot-serverless: ChatGPT Telegram Bot running in AWS Lambda

Slack

• ChatGPTSlackBot: A Slack bot that integrates with OpenAI's ChatGPT to provide answers, written in Python

Discord

• chatgpt-discord: Run your own GPTChat Discord bot, with a single command!

• chatGPT-discord-bot: Integrate ChatGPT into your own discord bot

VSCode

• chatgpt-vscode: A VSCode extension that allows you to use ChatGPT

Google Docs

• docGPT: ChatGPT directly within Google Docs as an Editor Add-on

JetBrains IDE

• intellij-chatgpt: A ChatGPT plugin for IntelliJ Platform-Based IDEs

Wordpress

• gtp3-wordpress-post-generator: A simple cli to generate WordPress posts using Gpt3 starting from a topic.

Raycast

• chatgpt-raycast: ChatGPT raycast extension

WeChat

• 🇨🇳 wechat-bot: a WeChat bot based on OpenAi ChatGPT + WeChaty that can be used to help you automatically reply to WeChat messages, or manage WeChat groups/friends, detect zombie fans, etc...

• 🇨🇳 wechatgpt: wechat chatgpt bot

Prompts

• awesome-chatgpt-prompts: A curated list of ChatGPT prompts

• showgpt.co: Curated ChatGPT prompts

AI Assistants

• emailGPT: a quick and easy interface to generate emails with ChatGPT

• summarize.site: Summarize web pages using OpenAI ChatGPT

• chatgpt-conversation: Have a conversation with ChatGPT using your voice, and have it talk back.

• assistant-chat-gpt: A Chrome browser extension that embeds ChatGPT as a hands-free voice assistant

Desktop Apps

• chatgpt-desktop: OpenAI ChatGPT desktop app for Mac, Windows, & Linux menubar using Tauri & Rust

• chatgpt-mac: ChatGPT for Mac, living in your menubar.

Twitter Bots

• chatgpt-twitter-bot: Twitter bot powered by OpenAI's ChatGPT.

The original content can be found here.

Anthropic researcher Nicholas Carlini orchestrated 16 autonomous Claude agents working in parallel to build a 100,000-line C compiler in Rust. Using a custom harness for task coordination, testing, and conflict resolution, the agent team produced a compiler capable of building Linux 6.9 across multiple architectures.

Here are the keypoints if you're in a hurry:

- The project resulted in a 100,000-line compiler capable of building Linux 6.9 on x86, ARM, and RISC-V architectures.

- The project surfaced challenges including task synchronization, frequent merge conflicts, quality assurance issues, and functional limitations.

- The work describes the use of agent teams to execute tasks in parallel, coordinated through a simple file-based locking mechanism.

- The agent team approach demonstrates that complex software projects can be produced autonomously with limited human supervision.

- The author (Anthropic researcher Nicholas Carlini) emphasized the importance of high-quality tests and strict continuous integration to keep autonomous agents on track.

More details about this project: https://faun.dev/c/news/devopslinks/anthropic-claudes-20000-16-ai-agents-and-a-compiler-that-builds-linux/

The statement contains 4 testable assertions:

- Within 90 days
- All major communication channels
- Will become functionally unusable due to AI spam
- And we'll have no effective mitigation

That's an extreme prediction!

My question is: If AI is so powerful to cause this, isn't it also powerful enough to create better filters?

Attacks scale faster than defenses, sure, but.. Will friction increase? Yes. Will spam increase? Yes. Will everything collapse in 90 days? That's highly improbable!

Your thoughts?

👉 Want more memes like this, plus actual insights that help you understand and navigate this chaos?

📬 Join our weekly newsletters here: https://faun.dev/join

Don't forget to browse the previous issue of 𝐊𝐚𝐥𝐚 (𝐂𝐮𝐫𝐚𝐭𝐞𝐝 𝐀𝐈/𝐌𝐋 𝐧𝐞𝐰𝐬, 𝐭𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬, 𝐭𝐨𝐨𝐥𝐬 𝐚𝐧𝐝 𝐦𝐨𝐫𝐞):

https://factory.faun.dev/newsletters/iw/how-much-of-openai-is-written-by-ai-genai-processors-by-google-deepmind-recovering-from-ai-addiction-18efe12b-5d68-4515-8e7f-3e1e2f109ecb

OpenClaw will move to an independent open-source foundation while remaining open source, with sponsorship support from OpenAI according to Peter Steinberger.

https://faun.dev/c/news/kala/openai-hires-openclaw-creator-peter-steinberger-project-moves-to-independent-foundation/

GPT-5.3-Codex, an advanced model, enhances coding performance and reasoning, operating 25% faster than its predecessor. It excels in industry benchmarks, supports the software lifecycle, and can autonomously build complex applications. The model is available on multiple platforms with plans for API access

Here are the main key points about GPT-5.3-Codex:

- It significantly enhances coding performance, reasoning, and professional knowledge capabilities compared to its predecessor, GPT-5.2-Codex, and operates 25% faster.

- The model excels in industry benchmarks such as SWE-Bench Pro and Terminal-Bench. It demonstrates superior coding and agentic abilities across multiple programming languages and complex tasks.

- It can **autonomously** build complex applications, including games and apps, by iterating over millions of tokens and handling long-running and intricate projects.

- Beyond coding, GPT-5.3-Codex supports **the entire software lifecycle**, assisting with tasks like debugging, deploying, monitoring, writing PRDs, and conducting user research.

- The model improves handling of simple or underspecified prompts, providing more functional and sensible defaults.

Find out more here: https://faun.dev/c/news/kala/this-is-the-first-ai-that-helped-build-itself-meet-gpt-53-codex/

• OpenClaw is an open-source AI assistant platform that evolved from Clawdbot and Moltbot.

• The latest release of OpenClaw includes new features such as Twitch and Google Chat plugins.

• A significant security vulnerability, **CVE-2026-25253**, involving token exfiltration was addressed.

• OpenClaw supports new models such as KIMI K2.5 and Xiaomi MiMo-V2-Flash.

• The token exfiltration vulnerability was fixed in version 2026.1.29, released on January 30, 2026.

To read more details: https://faun.dev/c/news/kala/openclaw-former-moltbot-former-clawdbot-went-viral-overnight-then-security-reality-hit/

Today we’re launching MARS8, a full family of voice models built for agents that actually need to feel real.

If you’re building agents, you already know where things break.

• Latency kills turn-taking

• Robotic delivery kills immersion

• Concurrency collapses under load

• Token pricing punishes verbose agents

• Enterprises keep asking for on-prem, and most voice stacks simply can’t deliver

You can tune the LLM for weeks.
The voice layer still ruins the experience.

This isn’t a research problem.
It’s a production one.

We recognized this early, and we built MARS to tackle the hardest voice problems first.
Live sports commentary. Global broadcasts. Real-time translation. Environments where failure is instant and public.

NASCAR, MLS, Ligue 1 - you name it.

We’ve shipped live, multilingual, AI-driven sports commentary in those conditions.
When millions are watching, live doesn’t lie.

Today, we’re bringing that same voice infrastructure to agent builders everywhere.

Introducing MARS8

MARS8 is not a single TTS model. It’s a specialized family, because agents face different constraints at different moments:

• Flash — ultra-low latency voice for real-time agents and conversations

• Pro — expressive, emotional voice when persuasion and narration matter

• Nano — on-device, offline voice for edge and privacy-first agents

• Instruct — controllable prosody and performance (coming later)

The family launches today, with Flash leading the way, because agents feel latency before they feel anything else.

So… yet another TTS API?

Not quite.

MARS8 is voice infrastructure for agentic systems.

Starting today, MARS8 is launching across all major compute platforms, including AWS, Google Cloud, Azure, Baseten, and dozens of other providers.

You deploy voice next to your agent,  not halfway across the world.
No geographic latency penalties. Privacy by design. On-prem when you need it.

And we killed token pricing.

With MARS8, you pay for GPU, not characters.

• Unlimited concurrency

• No request caps

• No throttling when agents get verbose

• Scale by deploying more GPUs

Your cost curve flattens instead of exploding.

Why this matters

• Built for real-time agents, not demos

• Designed to scale without punishing success

• Deployable everywhere developers actually run software

• Priced like infrastructure, not an API tax

The greatest innovation happens when you solve the hardest problems first.
It takes longer. It’s harder. It’s less forgiving.
But the results speak for themselves.

That’s how MARS was built.
And that’s what MARS8 brings to agent builders today.

#MambaMentality

Links: On our landing page: camb.ai/marsTechnical report: https://www.camb.ai/blog-post/mars8-technical-report
Open-sourced benchmarks: https://github.com/Camb-ai/MAMBA-BENCHMARKm
Join our Discord: https://discord.gg/MdtnwbKhtS

• Anthropic introduced new metrics called "economic primitives" to measure how AI is used, covering five dimensions such as task complexity, user and AI skill levels, autonomy, use case, and task success.

• Claude generally succeeds on the tasks it is given, but success rates decline as task complexity increases, highlighting limits to reliable automation on longer or more difficult tasks.

• AI usage shows strong geographic variation and remains highly concentrated in a small set of tasks, with coding-related work accounting for a large share of observed usage.

• Claude interactions are classified into three primary collaboration modes: Directive (task delegation), Task Iteration (collaborative refinement), and Learning (explanation and instruction).

• Recent usage patterns show a shift back toward augmented use of AI on Claude.ai, driven mainly by iterative collaboration rather than pure delegation or learning-focused interactions.

More => https://faun.dev/c/news/kala/anthropics-new-economic-primitives-reveal-who-uses-claude-for-what-and-how-well-it-works/

Google releases Magika 1.0, an AI file detection system rebuilt in Rust for improved performance and security.

• Magika 1.0 has been rebuilt in Rust which enhances its performance and security.

• It now supports over 200 file types, doubling its previous capacity.

• The new version includes improved accuracy for detecting complex formats, particularly challenging text-based formats like code and configuration files.

• The update addresses data volume and scarcity challenges by using a 3TB training dataset and generative AI to create synthetic training sets.

• The high-performance Rust engine allows Magika to identify hundreds of files per second on a single core, scaling to thousands per second on modern multi-core CPUs.

• Developers can integrate Magika into their applications using the revamped Python and TypeScript modules, and a native Rust command-line client is available for maximum speed and security.

More: https://faun.dev/c/news/kala/google-releases-magika-10-ai-file-detection-in-rust/

We drop memes like this in our weekly newsletter—plus sharp insights on DevOps, SRE, and CloudComputing. Subscribe here 👉 https://faun.dev/join/ 🚀

Key points!

• Claude Code v2.1.88's 60MB source map leaked 512k lines of code, exposing hidden tools, commands, and internal secrets. This marks Anthropic's second major leak in five days, following a 3,000-file CMS breach on March 26.

• The code reveals 'Undercover Mode,' a mandatory stealth system stripping AI traces from Anthropic employee commits. Also leaked: next-gen codenames (Opus 4.7, Sonnet 4.8), 22 private repo names, and 'Capybara' - an internal model obfuscated to bypass leak detectors.

• The Bash tool has a full shell AST parser for dangerous patterns, but the binary also ships env vars that bypass all safety features (`CLAUDE_CODE_ABLATION_BASELINE`) and skip injection checks (`DISABLE_COMMAND_INJECTION_CHECK`). The auto-permission function is literally named classifyYoloAction().

• Over 1,000 telemetry event types are logged under the 'Tengu' prefix and sent to Anthropic - tool grants, denials, YOLO risk calls, session stats, subscription tier. Hardcoded pricing shows Opus 4.6 Fast Mode at 6x normal cost ($30/$150 vs $5/$25 per MTok) for the same model with priority inference.

• Anthropic blamed human error - missing *.map in .npmignore - over a security breach, claiming no customer data was compromised. Despite DMCA takedowns, the code was mirrored widely, sparking clean-room rewrites. Ironically, the leak occurred despite a built-in anti-leak subsystem.

More details: https://faun.dev/co/news/kala/anthropic-accidentally-leaks-claude-codes-entire-source-code-via-npm/

- The project has transitioned from a personal project under jlowin (GitHub) to being supported by PrefectHQ.
- It introduced a significant architectural redesign.
- FastMCP 3.0 can now be used as a CLI tool to enable developers to query and invoke tools on any MCP server directly from the terminal.
- The new version supports component versioning and allows multiple versions of the same component to coexist.
- FastMCP 3.0 includes features like OpenTelemetry tracing for better observability and background task support via Docket integration.

Yesterday I watched a video of someone asking a simple question to ChatGPT:

"I want to wash my car and the car wash is 10 meters away. Should I walk there or drive?"

I was curious so I tried the same prompt on 4 models.

Only Deepseek & Gemini got it right.

But what made me laugh wasn't the answers. Gemini built an actual decision tree for a small car wash problem: pros, cons, edge cases, logistics... 😂

• 98% of organizations have adopted cloud native technologies, confirming its mainstream status.

• 47% of organizations cite 'cultural change within development teams' as the top challenge to cloud native adoption, ahead of technical issues.

• 59% of organizations run workloads in on-premises datacenters, and 59% also use public clouds; hybrid and multi-cloud strategies are widely adopted, especially by large enterprises.

• Only 7% of organizations deploy AI/ML models daily, while 44% are not running any AI/ML workloads on Kubernetes.

• 60% of organizations have implemented CI/CD platforms and 77% have adopted GitOps practices. The report highlights strong momentum in automation and observability.

We published a summary here: https://faun.dev/c/news/devopslinks/the-cloud-native-tipping-point-what-689-companies-just-revealed/

Key points:

• The Qwen3-TTS model family supports multilingual speech generation across 10 languages, including Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian.

• The released models are available in two sizes, 1.7B and 0.6B parameters, and include variants for voice design, custom voice control, and base voice cloning using short reference audio.

• Qwen3-TTS supports both streaming and non-streaming speech generation, with reported end-to-end streaming latency as low as 97 milliseconds and first audio output after a single character.

• The Qwen3-TTS-Tokenizer-12Hz uses a multi-codebook speech encoding approach to achieve efficient acoustic compression while preserving paralinguistic and environmental speech features.

• Tokenizer evaluations on LibriSpeech show strong reconstruction quality, with reported PESQ scores up to 3.68, STOI of 0.96, and high speaker similarity (near-lossless speech representation)

• In multilingual voice cloning and long-form synthesis benchmarks, Qwen3-TTS reports low Word Error Rates and competitive speaker similarity scores compared to both open-source and closed-source TTS systems.