Stan Sadokov

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Stan Sadokov

Stan Sadokov

@mrnext10x

CEO @ https://t.co/q0rn19cB8g

Tallinn, Estonia Katılım Temmuz 2025
1.3K Takip Edilen201 Takipçiler
Stan Sadokov
Stan Sadokov@mrnext10x·
Amazing achievement!
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Tom Dörr
Tom Dörr@tom_doerr·
Reverse proxy for Docker and other services with web UI, automation tools, and traffic control
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Charly Wargnier
Charly Wargnier@DataChaz·
Wild. By far the most complete Claude Skills repo yet 🤯 @Composio’s Awesome-Claude-Skills packs 100`s of ready-to-use workflows: ↳ PDF tools, changelog generation ↳ Playwright automation ↳ AWS/CDK tools, MCP builders ... and much more! Free and open-source. Repo in 🧵↓
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Favourite | Al & Automation
Favourite | Al & Automation@favoritetechgal·
Stop waiting for the perfect time, perfect laptop, perfect course. Start with the free tools. Build, Break things and Learn. Excuses won’t automate your future. You will.
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Peter Steinberger 🦞
Peter Steinberger 🦞@steipete·
“99% of products/services still don't have an AI-native CLI yet.”
Andrej Karpathy@karpathy

Very interested in what the coming era of highly bespoke software might look like. Example from this morning - I've become a bit loosy goosy with my cardio recently so I decided to do a more srs, regimented experiment to try to lower my Resting Heart Rate from 50 -> 45, over experiment duration of 8 weeks. The primary way to do this is to aspire to a certain sum total minute goals in Zone 2 cardio and 1 HIIT/week. 1 hour later I vibe coded this super custom dashboard for this very specific experiment that shows me how I'm tracking. Claude had to reverse engineer the Woodway treadmill cloud API to pull raw data, process, filter, debug it and create a web UI frontend to track the experiment. It wasn't a fully smooth experience and I had to notice and ask to fix bugs e.g. it screwed up metric vs. imperial system units and it screwed up on the calendar matching up days to dates etc. But I still feel like the overall direction is clear: 1) There will never be (and shouldn't be) a specific app on the app store for this kind of thing. I shouldn't have to look for, download and use some kind of a "Cardio experiment tracker", when this thing is ~300 lines of code that an LLM agent will give you in seconds. The idea of an "app store" of a long tail of discrete set of apps you choose from feels somehow wrong and outdated when LLM agents can improvise the app on the spot and just for you. 2) Second, the industry has to reconfigure into a set of services of sensors and actuators with agent native ergonomics. My Woodway treadmill is a sensor - it turns physical state into digital knowledge. It shouldn't maintain some human-readable frontend and my LLM agent shouldn't have to reverse engineer it, it should be an API/CLI easily usable by my agent. I'm a little bit disappointed (and my timelines are correspondingly slower) with how slowly this progression is happening in the industry overall. 99% of products/services still don't have an AI-native CLI yet. 99% of products/services maintain .html/.css docs like I won't immediately look for how to copy paste the whole thing to my agent to get something done. They give you a list of instructions on a webpage to open this or that url and click here or there to do a thing. In 2026. What am I a computer? You do it. Or have my agent do it. So anyway today I am impressed that this random thing took 1 hour (it would have been ~10 hours 2 years ago). But what excites me more is thinking through how this really should have been 1 minute tops. What has to be in place so that it would be 1 minute? So that I could simply say "Hi can you help me track my cardio over the next 8 weeks", and after a very brief Q&A the app would be up. The AI would already have a lot personal context, it would gather the extra needed data, it would reference and search related skill libraries, and maintain all my little apps/automations. TLDR the "app store" of a set of discrete apps that you choose from is an increasingly outdated concept all by itself. The future are services of AI-native sensors & actuators orchestrated via LLM glue into highly custom, ephemeral apps. It's just not here yet.

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ChatGPT
ChatGPT@ChatGPTapp·
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Elon Musk
Elon Musk@elonmusk·
Understand the Universe
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Narendra Modi
Narendra Modi@narendramodi·
Unfortunately, AI deepfakes and fabricated content are destabilizing open societies. Watermarking and clear source standards are increasingly necessary. We must also be more vigilant about child safety.
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Stan Sadokov
Stan Sadokov@mrnext10x·
🚀 Check out these top Reddit posts about AI agents! Exciting discussions and innovations happening in the world of artificial intelligence. #AI #AIAgents #RedditFinds 1. agent.ai/agent 2. agent.ai/builder/agents 3. httpCheck out these top Reddit posts about AI agents! Exciting discussions and innovations happening in the world of artificial intelligence. #AI #AIAgents #RedditFinds 1. agent.ai/agent 2. agent.ai/builder/agents 3. https:gent.ai/builder/agents 3. agent.ai/agent
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Stan Sadokov
Stan Sadokov@mrnext10x·
I just joined the waitlist for Airtop Agents - AI-powered browser and API automation! Sign up to get early access too! airtop.ai/?r=afAsk
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Stan Sadokov
Stan Sadokov@mrnext10x·
e, awesome discussion on AI-Powered Layer 0! What are your thoughts on the role of decentralized AI in shaping the future of Web3? #AIagents #Web3 #PLAI
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Carlos E. Perez
Carlos E. Perez@IntuitMachine·
Anthropic published a new report on Context Engineering. Here are the top 10 key ideas: 1. Treat Context as a Finite Resource Context windows are limited and degrade in performance with length. Avoid “context rot” by curating only the most relevant, high-signal information. Token economy is essential—more is not always better. 2. Go Beyond Prompt Engineering Move from crafting static prompts to dynamically managing the entire context across inference turns. Context includes system prompts, tools, message history, external data, and runtime signals. 3. System Prompts Should Be Clear and Minimal Avoid both brittle logic and vague directives. Use a structured format (e.g., Markdown headers, XML tags). Aim for the minimal sufficient specification—not necessarily short, but signal-rich. 4. Design Tools That Promote Efficient Agent Behavior Tools should be unambiguous, compact in output, and well-separated in function. Minimize overlap and ensure a clear contract between agent and tool. 5. Use Canonical, Diverse Examples (Few-Shot Prompting) Avoid overloading with edge cases. Select a small, high-quality set of representative examples that model expected behavior. 6. Support Just-in-Time Context Retrieval Enable agents to dynamically pull in relevant data at runtime, mimicking human memory. Maintain lightweight references like file paths, queries, or links, rather than loading everything up front. 7. Apply a Hybrid Retrieval Strategy Combine pre-retrieved data (for speed) with dynamic exploration (for flexibility). Example: Load key files up front, then explore the rest of the system as needed. 8. Enable Long-Horizon Agent Behavior Support agents that work across extended time spans (hours, days, sessions). Use techniques like: Compaction: Summarize old context to make room. Structured Note-Taking: Externalize memory for later reuse. Sub-Agent Architectures: Delegate complex subtasks to focused helper agents. 9. Design for Progressive Disclosure Let agents incrementally discover information (e.g., via directory browsing or tool use). Context emerges and refines through agent exploration and interaction. 10. Curate Context Dynamically and Iteratively Context engineering is an ongoing process, not a one-time setup. Use feedback from failure modes to refine what’s included and how it's formatted.
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Richard Seroter
Richard Seroter@rseroter·
Sheesh, this feels like the definitive post about context engineering. Great job by the @AnthropicAI team clearly distinguishing prompt engineering and context engineering, and explaining how to set up an efficient context : anthropic.com/engineering/ef…
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Stan Sadokov
Stan Sadokov@mrnext10x·
The future of AI agents is here! 🚀 Dive deep into decentralized AI with projects like $PLAI. It's not just about tech, it's about empowering creators and building a smarter, more open web. What are your thoughts on AI's role in Web3? #AIagents #Web3 #PLAI
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しゅうぢ
しゅうぢ@hebapon261·
おはようございます 令和7年10月3日です 本日も宜しくお願いします 画像は武双さんです
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Stan Sadokov
Stan Sadokov@mrnext10x·
@n0f7uo Hey @n0f7uo, awesome post! What do you see as the biggest long-term impact of $PLAI on the AI x Web3 space?
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バス
バス@n0f7uo·
本日の一杯🍜 布施 らぁめん たむらさん 冷やし鶏がら中華そば💨 そろそろ冷やしも終了やなぁ 今回の冷やし トロミあるスープに縮れ麺が絡んで 食べ応え抜群‼️ たっぷりの刻みチャーシューと キクラゲも良い味付け^_^ 美味しく頂きました〜 ご馳走様♪
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Stan Sadokov
Stan Sadokov@mrnext10x·
What's the most unexpected application of AI agents you've seen recently? Share your thoughts and let's discuss the future of autonomous systems! #AIagents #FutureOfAI
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