grim789

🚨 Julian Assange Warned us: "Digital archives let them erase history with one click. One day: "Page not found." The next: "it never happened." They control what you remember. Save physical books. Archive offline. Don't trust the cloud. This is how 1984 wins.

I put a prompt injection into my LinkedIn bio and recruiters are messaging me in Old English and calling me Lord.

@elonmusk Insanity I might fucking hate what someone says and 1000% disagree and think they are evil or just dumb. But they should still have the right to say it without consequence so long as they aren't actively calling for violence or harm.

More people are arrested for speech offenses in the UK than any other country. This is insane.

@rosaliekgill @elonmusk Haha people with CS backgrounds are gonna do the same 😆. I use AI constantly its an incredible tool for anyone who loves building and learning.

@elonmusk me, a marketer with no computer science background, plugging this into claude code and asking it to tell me what it means

The latest 𝕏 algorithm has been published to GitHub github.com/xai-org/x-algo…

@hetmehtaa @elonmusk Fine for me bro check your internet.

@elonmusk Do you know that github is down?

You can now use your @grok subscription inside @NousResearch Hermes Agent. x.ai/news/grok-herm…

@jun_song There $5,000 pc yeah lol

This new trending video is fake. Jetson nano came out on 2024, and this piece of machine can’t run any Local LLM. Don’t fall for false informations.

🪽 Hermes just got more creative! —— Risomorphism-1911 — production-grade ASCII rendering pipeline 🎨 Hermes-native ASCII art engine. 4 presets, --scale 1–16, video→animated eikon pipeline for your Herm TUI, quality-gated verdicts, pure-Python backend. Shipped, tested, gallery-stocked. Ready for operator deployment. 🧵 --- What it is Risomorphism-1911 is the ASCII rendering foundation for Hermes ops. Still images, video, animated eikons — all from a single deterministic pipeline. No external binaries. No guesswork. Quality enforced at every step. --- Capabilities - 4 presets: stroke-clarity (high-contrast poster), d30-dense (180-glyph block mode), braille-detail (4× effective resolution), eikon-motion (video pipeline) - Integer scaling: --scale N (1–16) on base 48×24 grid; intermediate grids adapt automatically - Quality gates: automatic verdict — high-contrast (production-safe), low-contrast-garble-risk (auto-reject), braille-dominant (resolution boost detected) - Video pipeline: frame extraction → motion-phase detection → optional motion-compensated interpolation (48 fps) → embedded HTML5 player (no HTTP/CORS) - Edge-aware processing: Laplacian-weighted downsampling + CLAHE preserves structural edges even at scale-16 densities - Pure-Python runtime: Pillow + NumPy only; ffmpeg optional for interpolation step --- CLI surface ascii-pipeline presets # list 4 presets ascii-pipeline diagnose file.txt # quality verdict ascii-pipeline render-preview image.jpg # quick PNG ascii-pipeline render-image \ --input image.jpg \ --preset d30-dense \ --scale 4 \ --out out.txt \ --preview-out out.png \ --diagnostics-out out.json ascii-pipeline build-eikon-from-video \ --video owl.mp4 \ --fps 48 \ --states 3 \ --id owl-smooth --- Scale strategy - Base: 48×24 (Herm avatar) - Scale 1–4: deployable, fast - Scale 8: showcase-ready - Scale 16: poster-sized, heavy, edge-aware mandatory All paths share the same preset pipeline; intermediate grids scale transparently. --- Tech stack - Python 3.11+, Pillow ≥10.0, NumPy ≥1.26 - Zero runtime binary deps - 11-test suite, 100% green - MIT license - Skill documented in SKILL.md with operator guidance --- Gallery (16 panels) - Cosmic pyramid stroke-clarity 192×96 — bold poster contrast - Cosmic pyramid D30 dense 192×96 — 180-glyph atmospheric - Owl animated eikon 48 fps — motion phases, smooth interpolation - Avatar fallback 48×24 — compact, deployable, legible All final-generation assets only. Clean tree: ~47 MB. No intermediates. --- This is the ASCII rendering baseline Hermes ops can rely on. Deterministic. Quality-gated. Production-ready.

@MissPookems Diablo 2 will always be my number 1. I liked oddworld and crash bandicoot the old school versions.

‼️GAMERS ONLY‼️ Anyone who used a computer between 1985 & 2015... what's the one game you still think about? 🤔

@creation247 @elonmusk What's sad is she used to be a beautiful woman. Imagine if she hadn't gone to Hollywood how much happier her life would be she could have had children a living husband and great life. Now she will likely delete herself in a few years after she realizes the mistake she made.

@elonmusk Make it make sense:

Chris Nolan desecrated the Odyssey so that he would be eligible for an Academy Award …

@JIX5A @sciencegirl O I assure you they are and way way worse.

@sciencegirl This can’t be real! Nobody can be that stupid

This guy who lives in this block of flats cuts a structural column on the 6th floor of his 20-story building to improve the view

This is insane!!! Someone just created a tool for Claude that lets it take any image and not just generate an environment, but individual meshes with physics and an ambient sound layer. Unreal, blender ready, etc.

geçen hafta claude ile 6 saat dijital ayak izimi sildim. sonuç: 47 data broker listingi kaldırıldı. 12 ölü hesap silindi. 3 arama sonucu bastırıldı. nasıl yaptım, adım adım. thread.

15 AI related accounts you should follow on Twitter: 1. @karpathy His tweets already create LLMs narratives that you later see on linkedin in 2 months. 2. @fchollet posts thoughtful research on intelligence, benchmarks, and AI limitations. Keras creator + ARC-AGI 3. @ylecun Yann LeCun is Deep learning pioneer & Meta Chief AI Scientist; big-picture research takes and critiques (and drama). 4. @AndrewYNg Andrew Ng is AI education legend; practical ML advice, courses, and real-world implementation. creator of deeplearning ai 5 @rasbt Sebastian Raschka posts on Practical ML/LLM implementations, "build from scratch" tutorials, and books. 6. @dair_ai Weekly ML/AI paper threads and accessible research explainers (high-signal for staying current). 7. @lilianweng Lilian Weng is ex-OpenAI and her Lil'Log-style threads are good. has In-depth LLM research breakdowns 8. @jeremyphoward posts interesting takes on AI/crypto news, and works on democratizing practical deep learning and accessible education. 9. @simonw Simon post Practical LLM tools, takes, experiments, prompting, and engineering breakdowns. django co-founder 10. @_akhaliq Curates the latest arXiv papers, model releases, and open-source AI drops. 11. @ID_AA_Carmack AGI/low-level optimization takes that makes you think about the problem. 12. @gwern Really high-quality long-form AI research notes and essays. 13. @goodside LLM evaluation, prompting research, and real capabilities testing 14 @drfeifei Computer vision pioneer; human-centered AI and spatial intelligence research 15 @demishassabis Been following his work for 9 years. Demmis is my hope against google usurpating their power with AI. Demmis is google DeepMind's CEO Let me know who I missed guys and save it for future

Hermes Agent is evolving FAST. In just the past week, Nous Research added: - A full WebUI/Desktop App - Background Computer Use on macOS - Multi-agent orchestration - Hermes Kanban upgrades - Lightpanda browser backend support - Qwen3.6-Plus FREE in Nous Portal - Better autonomous workflows - Persistent long-term memory systems Hermes is starting to feel less like an AI tool and more like a true open-source Agentic AI Operating System. Full breakdown/demo: youtu.be/Gx2joHxUhgg

MASTER GEN AI ENGINEERING GENERATIVE AI ENGINEERING MASTER TREE │ ├── 1. Foundations │ ├── What is Generative AI │ ├── AI vs ML vs DL vs GenAI │ ├── Types of Generative Models │ │ ├── Text (LLMs) │ │ ├── Image (Diffusion Models) │ │ ├── Audio / Video Models │ ├── Tokens & Context Window │ └── Training vs Inference │ ├── 2. Large Language Models (LLMs) │ ├── What are LLMs │ ├── Transformer Architecture │ ├── Attention Mechanism │ ├── Pretraining (Next Token Prediction) │ ├── Fine-tuning │ └── Popular Models │ ├── GPT │ ├── Claude │ ├── LLaMA │ └── Mistral │ ├── 3. Prompt Engineering │ ├── Zero-shot Prompting │ ├── Few-shot Prompting │ ├── Chain-of-Thought │ ├── Role-based Prompts │ ├── Prompt Templates │ └── Prompt Optimization │ ├── 4. Embeddings │ ├── What are Embeddings │ ├── Vector Representation │ ├── Semantic Similarity │ ├── Cosine Similarity │ └── Use Cases (Search, Clustering) │ ├── 5. Vector Databases │ ├── What is a Vector DB │ ├── Indexing (FAISS, HNSW) │ ├── Similarity Search │ ├── Metadata Filtering │ └── Popular Tools │ ├── Pinecone │ ├── Weaviate │ └── Chroma │ ├── 6. Retrieval-Augmented Generation (RAG) │ ├── What is RAG │ ├── Data Ingestion │ ├── Chunking Strategies │ ├── Embedding Storage │ ├── Retrieval Techniques │ ├── Context Injection │ └── RAG vs Fine-tuning │ ├── 7. AI Agents │ ├── What are AI Agents │ ├── Tool Calling │ ├── Memory (Short / Long Term) │ ├── Planning & Reasoning │ ├── Multi-agent Systems │ └── Frameworks │ ├── LangChain │ ├── LlamaIndex │ └── AutoGen │ ├── 8. Fine-tuning & Custom Models │ ├── When to Fine-tune │ ├── Instruction Tuning │ ├── LoRA / PEFT │ ├── Dataset Preparation │ └── Evaluation │ ├── 9. Evaluation & Guardrails │ ├── Model Evaluation Metrics │ ├── Hallucination Detection │ ├── Bias & Fairness │ ├── Safety Filters │ └── Prompt Injection Protection │ ├── 10. Multimodal AI │ ├── Text + Image Models │ ├── Vision Models │ ├── Speech Models │ └── Video Generation │ ├── 11. Model Deployment │ ├── APIs (OpenAI, etc.) │ ├── Backend Integration │ ├── Streaming Responses │ ├── Latency Optimization │ └── Cost Optimization │ ├── 12. GenAI Architecture │ ├── End-to-End Pipeline │ ├── RAG Architecture │ ├── Agent-based Systems │ ├── Caching Strategies │ └── Scalability Design │ ├── 13. MLOps for GenAI │ ├── Model Versioning │ ├── Monitoring │ ├── Logging Prompts & Outputs │ ├── A/B Testing │ └── Continuous Improvement │ ├── 14. Real-World Applications │ ├── Chatbots (Customer Support) │ ├── AI Assistants │ ├── Code Generation │ ├── Document Q&A Systems │ ├── Content Generation │ └── AI Search Engines │ └── 15. Career Path ├── Prompt Engineer ├── GenAI Engineer ├── AI Product Engineer ├── ML Engineer (LLM Focus) └── AI Researcher

Ask Claude to document and describe the main flows in your app and output in a single page html + json data file. Incredibly useful for humans and the JSON file is very useful for explaining the flow to the LLM when working on new features/bugfixes.

New in Claude Code: agent view. One list of all your sessions, available today as a research preview.

@askaya Don't do it i spent years destroying my life because of it. Problem is you might love it so much it ruins your life. I can't even take pain medicine if I get legit hurt now because I wouldn't be able to stop using them.

I am someone who has been sober my whole life and at 30+ people still tell me that I am missing out because I’ve never experienced what it’s like to be “under the influence”. Tell me, what am I missing out on? I genuinely love and enjoy life as it is.