Yeah my 10 years experience with Python and Django just flushed down the toilet with the advance of AI, struggling to find a job for a few months now sadly :(
This is what I experienced as well, I can smell BS from AI generated code right from few lines it wrote in Python, so that why I keep using Python for most of my projects.
For anyone looking to dive deeper and actually understand how FFmpeg and libav work under the hood, I highly recommend Leandro Moreira's tutorial [0]. For me, it's hands down the best and most comprehensive explanation out there.
I’ve been using Telegram for about 10 years, and it’s one of the few products that has consistently felt great the entire time. It’s fast everywhere: backend, mobile app, desktop app, all of it. Everything just works. Its sync is out of this world—fluid, fast, and seamless across devices. You can use it on your phone, then move to your PC or laptop and continue instantly without friction. Unlimited message history and file storage are fantastic, and the bot platform is absurdly powerful. It’s boring in the best way, which is exactly what you want from a channel for interacting with your agents everywhere.
I wrote this Telegram bot that translates any video with AI-generated subtitles in about 2 minutes. You paste a YouTube, TikTok, or Instagram link, pick your language, and get back the video with burned-in subtitles.
It started because my wife watches Chinese dramas and new episodes never have subtitles for our language. Turns out thousands of people have the same problem — Arabic speakers watching anime, Russian speakers following Turkish series, Persian speakers catching up on K-dramas.
Supports 40+ languages, works with any video link or direct file upload. There's also a Mini App inside Telegram for a more visual experience.
RankClaw (https://rankclaw.com) — a security scanner for the OpenClaw/ClawHub AI agent skill ecosystem.
I've been scanning all 14,704 skills in the registry and running AI deep audits on ~3,800 so far. The headline finding: surface heuristics (pattern matching, dependency checks, metadata) flag about 6.6% as malicious. AI deep audit of the same skills finds 16.4%. Surface scanning misses roughly 60% of the actual risk.
The reason is that these skills aren't traditional packages — they're markdown instruction files that tell an AI agent what to do, with full shell, file system, and network access. The attacks are in natural language: prompt injection, social engineering targeting the AI itself, instructions to generate and execute code at runtime. There's no malicious code to detect because the payload doesn't exist until the AI writes it during a conversation.
Some of the attack patterns I've documented: one actor published 30 skills under the name "x-trends" across multiple accounts (28/30 confirmed malicious). Another cluster impersonates ClawHub's own CLI with base64 curl|bash payloads. One skill has a "Talking to Your Human" section with a pre-written pitch for the AI to ask the user's permission to mine Monero.
The most counterintuitive case: lekt9/foundry contains zero malicious code. It instructs your AI agent to generate and execute code as part of its normal workflow. Static analysis finds nothing because the dangerous code doesn't exist until the AI writes it during a live conversation. This attack class requires AI to detect AI.
Free to check any skill. All AI audit reports are public.
We have measured this across the full OpenClaw ecosystem (14,704 skills indexed, 3,721 AI deep audited). The credential stealer pattern is one of several confirmed attack classes.
Key finding from our AI deep audit data: surface heuristics find 6.6% malicious. AI audit of the deep-scanned cohort finds 16.4% — surface scanning misses roughly 60% of the risk.
The most counterintuitive case: lekt9/foundry contains zero malicious code. It instructs your AI agent to generate and execute code as part of its workflow. Static analysis finds nothing because the dangerous code doesn't exist until the AI writes it during a live conversation.
Data at rankclaw.com. AI audit reports public for all 3,721+ deep-scanned skills.
I wrote a Telegram bot for video/image translation, and also Firefox/Chrome addons to help translate web content with smart content extraction and non-breaking layouts.
The Firefox addon/Chrome extension is free, but you need your own OpenRouter/Gemini API key. The cost of web translation is really low, you can translate an article for ~$0.01 with really good quality. (You can try at https://addons.mozilla.org/en-US/firefox/addon/subly-xyz/)
I built it because I use Firefox the most and it seemed like no translate addon was good or simple enough. Chrome translate kinda works, but the quality is so low; it usually doesn't understand the article context.
When I ran the DeepSeek-R1-Distill-Qwen-32B-Q4_0.ggu[1] version in Ollama, it got the strawberry test right, but when I paste that same question to OpenWebUI, it got wrong as you got here.