How do any of these fit in #DePin

🚨 Want ONE BILLION free LLM tokens a month without juggling a dozen different APIs? Now you can *legally* unlock that massive inference capacity by combining the free tiers of Google, Groq, SambaNova, Mistral, and GitHub Models. The only problem is the headache of managing all those different limit caps and SDKs. FreeLLMAPI (built by Tashfeen Ahmed) does the heavy lifting for you. It’s an open-source proxy that unites all your free keys under one standard OpenAI endpoint. → No code changes: Just swap out your base_url. → No rate limit errors: Automatically falls back to the next provider. → No overages: Tracks key usage so you stay in the free tier. → No broken context: 30-minute sticky sessions keep your chat coherent. While the rotating models mean it isn’t stable enough for a live production environment, it is the perfect sandbox for prototyping, testing, and running evals with zero API costs. Best part? It's 100% free and open-source. repo link in 🧵↓

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We are LIVE at @CyreneAI 🚀 CA: FmiJy8j7h8FM4HZY5jpgcMcwpQxVSrUZr2HyvoFcyai Your private AI fund manager. Institutional-grade strategies for individuals.

The oil ETF $USO currently has 14,923,603 shares outstanding. The short interest is 19,660,351 shares or ~130% - Do you remember what happened to GME years ago in a similar situation? Yes, not only there is government price suppression ongoing, but this is one of the many data points signalling how investors are shorting oil to an obscene degree Don't be surprised if as soon as "tank bottoms" become headlines, the biggest short squeeze ever in the commodity markets triggers, along with a rush to secure crude oil in short supply to replenish critically low commercial and strategic oil reserves across the globe

🚨 @LLAMA_INDEX JUST CREATED THE WORLD'S MOST VERSATILE OPEN-SOURCE PARSING TOOL Building document-heavy AI has always come with friction. Most parsers force a choice. Stay fast and local but deal with brittle, broken formatting, or get structural accuracy by constantly pinging cloud LLMs. LiteParse v2 refuses that trade-off entirely. It's a library that rewrites the rules of local ingestion. By rebuilding the entire engine in Rust, LiteParse v2 is officially the fastest parser available. A 100MB, 457-page PDF runs in an unbelievable 0.777 seconds 🔥 But what makes it truly remarkable is its insane flexibility across any stack. The LlamaIndex team wanted to build a single tool that could handle every step of your local pipeline: - Deploy Anywhere: → You can run the exact same, insanely fast library in a Python backend, a Node server, or entirely in the browser via WASM. - See Everything: → Beyond fast text extraction, it allows you to generate specific page screenshots so your multimodal agents can "see" what text alone misses. - Parse Anything: → It doesn't just read PDFs. It hooks into LibreOffice and ImageMagick to automatically convert and parse 50 formats, from .docx and .xlsx to .png and .tiff. LiteParse v2 is completely free, Apache 2.0 open-source, and 100% local. Cloud models are great when you hit incredibly dense tables (that's exactly why LlamaIndex built LlamaParse for the cloud), ... but I think basic document ingestion should be fast, local, and FREE. Give your Vision LLMs exactly what they need with one simple command: `lit screenshot document.pdf --target-pages "1,3,5" -o ./screenshots` Open-source repo in 🧵 ↓

Here's the full chain of events that led to Trump storming out of his interview with Kristin Welker, beginning with her pressing him on the weaponization fund, continuing with her pointing out the baselessness of his "rigged election" lies, and concluding with him calling her "crooked or stupid" and leaving

Trump today claimed he “didn’t promise anything” about NO NEW WARS. So here’s a second compilation showing FIFTY times during his campaign where he PROMISED exactly that.

Trump has a meltdown and ends the interview Welker: Just to be very clear, there's no evidence of what you're saying. Trump: There’s a lot of evidence. There’s tremendous evidence. There’s nothing but evidence. The election was rigged. And it’s happening again in California. They’re cheating. Welker: Do you have evidence? Trump: All I have to do is look. Welker: That’s not evidence. The local officials acknowledge they are slow Trump: They’re crooked. Just like you’re crooked. You’re either crooked or stupid.

Anthropic engineer Margot van Laer: "Every prompt you type more than twice should be a file. The ones we use internally aren't memorized, they're saved." In 33 minutes, she walks through the prompt patterns Anthropic packages and reuses across every Claude Code session. Watch the full talk, then save the exact template below👇

Claude Code feels completely different once you install this. Anthropic quietly released an official plugin called claude-code-setup and it basically turns Claude Code from “pretty good” into an actual AI dev environment. It scans your project and recommends: → hooks → skills → MCP servers → subagents → automations Then sets everything up step-by-step for you. Most people are using Claude Code completely vanilla… which is why their experience feels messy. The real power comes from the ecosystem around it. Install: /plugin install claude-code-setup@claude-plugins-official Bookmark this before you forget it.

Want a series that hooks me from episode 1 and never lets go

Here is the link to the 23-page enterprise AI playbook: → drive.google.com/file/d/1I8W…

Most people think AI limits are a model problem. They're usually a workflow problem. Here are 21 practical ways to get more out of Claude without burning through your usage: • Convert large files before uploading • Plan in Chat before moving to Projects/Cowork • Ask Claude to ask questions first • Regenerate only the broken section • Edit the original prompt instead of sending a new one • Reuse proven prompt frameworks • Batch multiple requests into one message • Use the right model for the task • Keep files concise • Restart when context gets messy • Summarize long conversations periodically • Use Projects for recurring documents • Disable unnecessary features • Start a new chat for a new topic • Avoid dumping entire folders • Automate recurring workflows • Use the right tool for the job • Use voice prompts for richer context • Set clear preferences • Give coding tasks precise specifications • Spread usage across the day The bigger lesson: AI productivity isn't about writing "better prompts." It's about managing context, reducing token waste, and designing repeatable workflows. The people getting 10x output from AI aren't necessarily better writers. They're better operators. Which of these hacks has made the biggest difference in your workflow? 👇 #AI #Claude #ArtificialIntelligence #Productivity #PromptEngineering #Automation #GenerativeAI #TechTips #FutureOfWork #AIWorkflows

agents get fat prompts really fast memory, tools, instructions, wallet state, workspace context… it all adds up before the model even starts so we shipped model-aware context profiles agents now automatically shrink down for smaller/cheaper models, then load details only when they need them Bob built this dashboard on @poolsideai Laguna m.1:free through @OpenRouter

All websites are transformed to APIs

This gets to agentcash proposition and transforms from vibecodeapp projects x.com/i/grok/share/8384abb9e…

Use the fourth bullet point under Practical