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Today, I’m open sourcing my Next.JS LLM Answer Engine Repo and video are now live!🎉 Built with amazing tech from: Vercel 🔺AI SDK Groq Inc ⚡️Inference @MistralAILabs 🧠Mixtral 🦜 JS OpenAI Embeddings Brave & Serper API 🔎 Search APIs Repo + YouTube 🎥 👇

Developers Digest

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172,311 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 10

Фото профиля Developers Digest

Developers Digest2 лет назад

🎥 Build a Next.JS LLM Answer Engine with Vercel AI SDK, Groq, Mistral, Langchain, OpenAI, Brave & Serper

Фото профиля Developers Digest

Developers Digest2 лет назад

⭐️

Фото профиля Developers Digest

Developers Digest2 лет назад

Just realized this is trending on GitHub 🙏❤️

Фото профиля Developers Digest

Developers Digest2 лет назад

❤️

Фото профиля Developers Digest

Developers Digest2 лет назад

🙏

Фото профиля Guillermo Rauch

Guillermo Rauch2 лет назад

@vercel @GroqInc @MistralAILabs @LangChainAI @OpenAI @brave @serperapi 🔥

Фото профиля Ed Weng

Ed Weng2 лет назад

@vercel @GroqInc @MistralAILabs @LangChainAI @OpenAI @brave @serperapi Thank you! Amazing work. I watched the YouTube video 💪🏻

Фото профиля Developers Digest

Developers Digest2 лет назад

@vercel @GroqInc @MistralAILabs @LangChainAI @OpenAI @brave @serperapi Thank you! 🙏

Фото профиля Divine Er

Divine Er2 лет назад

@GroqInc @vercel @MistralAILabs @LangChainAI @OpenAI @brave @serperapi Inspiring. Utilising @GroqInc .

Фото профиля nicoarq

nicoarq2 лет назад

@vercel @GroqInc @MistralAILabs @LangChainAI @OpenAI @brave @serperapi Is this basically your own perplexity?

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Today, I am open sourcing my latest project diving into AI Devices Tech: Groq Inc Llama3🦙(whisper sneak peak coming 🔜) & Langsmith🦜 OpenAI TTS, Vision, Whisper🤫 Vercel AI SDK & Next.js🔺 Serper API Search🔍 Upstash Rate Limiting🛑 fal Llava🌋 🎥 & Repo👇

Today, I am open sourcing my latest project diving into AI Devices Tech: Groq Inc Llama3🦙(whisper sneak peak coming 🔜) & Langsmith🦜 OpenAI TTS, Vision, Whisper🤫 Vercel AI SDK & Next.js🔺 Serper API Search🔍 Upstash Rate Limiting🛑 fal Llava🌋 🎥 & Repo👇

Developers Digest

50,153 просмотров • 2 лет назад

✨ Today I rebuilt 🏔️ AltaVista Search, the main search engine before Google existed The problem: if you press the 🔍Search button in MSIE4 it'll open a sidebar with AltaVista in it that opens: http:// home . microsoft . com/search/search.asp But that URL doesn't resolve anymore of course, so how do I make this work? I asked Claude Code and built a Dnsmasq DNS server that routes all regular DNS queries to Cloudflare but for home . microsoft . com it hijacks it and sends it to my own server Then I let Nginx respond to that doamin and show the Altavista Search sidebar, I had to rebuild the design from a video on YouTube because I couldn't find any actual archeological remains of it anymore Also Altavista Search of course doesn't exist anymore so it can't respond to queries either so I use a SERP API to query search results from Google and display them instead This is stuff I wouldn't even be close to doing before AI, I'd have no idea how to configure Dnsmasq to spoof queries like this The search engine results are proxied via my HTTP proxy so MSIE4 can open most sites (because old browsers don't support HTTPS and most JS!)

✨ Today I rebuilt 🏔️ AltaVista Search, the main search engine before Google existed The problem: if you press the 🔍Search button in MSIE4 it'll open a sidebar with AltaVista in it that opens: http:// home . microsoft . com/search/search.asp But that URL doesn't resolve anymore of course, so how do I make this work? I asked Claude Code and built a Dnsmasq DNS server that routes all regular DNS queries to Cloudflare but for home . microsoft . com it hijacks it and sends it to my own server Then I let Nginx respond to that doamin and show the Altavista Search sidebar, I had to rebuild the design from a video on YouTube because I couldn't find any actual archeological remains of it anymore Also Altavista Search of course doesn't exist anymore so it can't respond to queries either so I use a SERP API to query search results from Google and display them instead This is stuff I wouldn't even be close to doing before AI, I'd have no idea how to configure Dnsmasq to spoof queries like this The search engine results are proxied via my HTTP proxy so MSIE4 can open most sites (because old browsers don't support HTTPS and most JS!)

@levelsio

84,414 просмотров • 7 месяцев назад

Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.

Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.

Akshay 🚀

61,794 просмотров • 6 дней назад

Mind blown 2.0: Onchain quants just printed $400,000+ trading Polymarket bets on SPX, Dow, Russell 2000, AAPL, GOOG - all powered by open-source financial market simulations! The god-tier stack just dropped: Financial Datasets MCP Server (1.7k stars) + MiroThinker-H1 (88.2 benchmark, 7.1k stars) + MiroFish - multi-agent simulation engine built by a Chinese undergrad student in just 10 days (now 18k+ stars and scored millions in funding overnight). How each repo works and how to apply it: 1. Financial Datasets MCP Server -> Unlimited real historical prices, balance sheets, income statements and news for ANY ticker (SPX, AAPL, GOOG, Russell 2000 etc.). This is your free 40+ year data parser - just connect and pull raw facts. Repo: 2. MiroThinker-H1 -> Deep research agent (pulls data straight from the MCP Server). Analyzes latest 10-K, Fed minutes, earnings, geopolitics and builds 500+ clean, verified datasets. Without it your simulation is garbage - it turns raw data into the perfect “brain” for the engine. Repo: 3. MiroFish -> The actual multi-agent simulation engine. Load the dataset from MiroThinker and run thousands of AI agents with different personalities (macro strategist, sentiment analyst, panic buyer etc.). Get probability cones and the full "matrix" - exactly how price will react to any event. Repo: Key Applications: .Trading: throw in any event -> simulate crowd reaction -> catch the edge and ape on Polymarket .Macro forecasting: test global events before they hit the news .Easy setup: Docker + any LLM API, live in 10-15 minutes Pro tip: Feed MiroThinker latest 10-K or breaking news -> it builds 500+ verified scenario datasets -> load into MiroFish -> get probability cones for next-week price moves. Then ape the highest-conviction side on Polymarket risk-free. Traders are already winning big: [superstonksbro] -> PnL = $182k, multiple $20k+ wins. [CamelUp] -> PnL = $193k, 2.4k+ predictions. Both crushing it with this exact stack. For effortless gains, try Kreo copy-trading: auto-mirror these new simulation beasts and ride their edges. Try here: Add their wallets: 0x17559efac103ac7f361be37ec0b93888d4c55aac // 0x969fae0a3a93778adc42178f72c612ed8c4e4d55 to [ and start track/copy them right now. Save this links and info so you don't lose it.

Mind blown 2.0: Onchain quants just printed $400,000+ trading Polymarket bets on SPX, Dow, Russell 2000, AAPL, GOOG - all powered by open-source financial market simulations! The god-tier stack just dropped: Financial Datasets MCP Server (1.7k stars) + MiroThinker-H1 (88.2 benchmark, 7.1k stars) + MiroFish - multi-agent simulation engine built by a Chinese undergrad student in just 10 days (now 18k+ stars and scored millions in funding overnight). How each repo works and how to apply it: 1. Financial Datasets MCP Server -> Unlimited real historical prices, balance sheets, income statements and news for ANY ticker (SPX, AAPL, GOOG, Russell 2000 etc.). This is your free 40+ year data parser - just connect and pull raw facts. Repo: 2. MiroThinker-H1 -> Deep research agent (pulls data straight from the MCP Server). Analyzes latest 10-K, Fed minutes, earnings, geopolitics and builds 500+ clean, verified datasets. Without it your simulation is garbage - it turns raw data into the perfect “brain” for the engine. Repo: 3. MiroFish -> The actual multi-agent simulation engine. Load the dataset from MiroThinker and run thousands of AI agents with different personalities (macro strategist, sentiment analyst, panic buyer etc.). Get probability cones and the full "matrix" - exactly how price will react to any event. Repo: Key Applications: .Trading: throw in any event -> simulate crowd reaction -> catch the edge and ape on Polymarket .Macro forecasting: test global events before they hit the news .Easy setup: Docker + any LLM API, live in 10-15 minutes Pro tip: Feed MiroThinker latest 10-K or breaking news -> it builds 500+ verified scenario datasets -> load into MiroFish -> get probability cones for next-week price moves. Then ape the highest-conviction side on Polymarket risk-free. Traders are already winning big: [superstonksbro] -> PnL = $182k, multiple $20k+ wins. [CamelUp] -> PnL = $193k, 2.4k+ predictions. Both crushing it with this exact stack. For effortless gains, try Kreo copy-trading: auto-mirror these new simulation beasts and ride their edges. Try here: Add their wallets: 0x17559efac103ac7f361be37ec0b93888d4c55aac // 0x969fae0a3a93778adc42178f72c612ed8c4e4d55 to [ and start track/copy them right now. Save this links and info so you don't lose it.

slash1s

35,001 просмотров • 5 месяцев назад

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️

raullen

33,889 просмотров • 13 дней назад

I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:

I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:

Lee Robinson

118,242 просмотров • 1 год назад

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

102,223 просмотров • 7 месяцев назад

Since I posted my Personal OS / filesystem article, LLM personal knowledge bases have turned into a real topic in the AI world. I’ve been building this system in Cursor for almost two years, but I wasn’t expecting to end up talking with people like a YouTube co-founder, a NASCAR driver, or some of the AI leaders I respect most because of that post. For me it was the first signal that this wasn’t niche anymore. The biggest pushback on the article was whether a filesystem is enough or scalable for something like this. Scaling the vault is easy; scaling curation and placement is not. Karpathy’s LLM Wiki published soon after with same thesis and it was an independent validation for me. "LLM incrementally builds and maintains a persistent wiki structured, interlinked markdown between you and the raw sources." Now there are tons of similar projects, different takes on the same idea. That’s good, I’m also evolving my own stack from what’s out in the open, and honestly, reframing the personal filesystem as a wiki is a smart move. I’m posting this because I think the harder problem is still the knowledge transfer pipeline. Designing a Personal OS (aka personal knowledge base) is the easy part. The architecture only starts to pay off when you fill it for years -not just posts you liked, but decision patterns, career and life details, half-formed thoughts, writing, the messy stuff. Getting all of that into the right markdown file, at the right time, in the right shape is still the bottleneck. I built a Chrome extension (Feed2Context, details in the article) that grabs a post with my notes from my feed, drops it into the filesystem, and my agents synthesize and route it. I also built OpenHome assistant as a voice pipeline from my room into the wiki. Plus a bunch of MCP hooks into my accounts. But orchestrating all these helpers gets exhausting. A lot of people suggested Obsidian but I'm mostly on Readwise CLI to pull from X, LinkedIn, arXiv, books, and news. It works well on mobile, and because it’s a CLI, agents can find what they need and push it into the filesystem. Skill registries help a lot, in the videos I’ve got flows like Readwise CLI + alphaXiv MCP for research papers: save a paper, agent pulls the full text, analyzes it, teaches me back. I’m also testing Zapier CLI, and waiting for especially the Triggers API, between things like Yutori or plain cron, keeping a personal wiki alive is still hard; nobody wants to be the cron job for their own life, so triggers might be part of the answer. TL;DR: A personal filesystem you control isn’t optional if you don’t want to rent your memory from one AI company. The open problem is keeping it fed and current. What I actually want is one solution that can watch my screen, hear my voice, read my accounts, and write into my Personal OS without me acting as the integration layer forever.

Since I posted my Personal OS / filesystem article, LLM personal knowledge bases have turned into a real topic in the AI world. I’ve been building this system in Cursor for almost two years, but I wasn’t expecting to end up talking with people like a YouTube co-founder, a NASCAR driver, or some of the AI leaders I respect most because of that post. For me it was the first signal that this wasn’t niche anymore. The biggest pushback on the article was whether a filesystem is enough or scalable for something like this. Scaling the vault is easy; scaling curation and placement is not. Karpathy’s LLM Wiki published soon after with same thesis and it was an independent validation for me. "LLM incrementally builds and maintains a persistent wiki structured, interlinked markdown between you and the raw sources." Now there are tons of similar projects, different takes on the same idea. That’s good, I’m also evolving my own stack from what’s out in the open, and honestly, reframing the personal filesystem as a wiki is a smart move. I’m posting this because I think the harder problem is still the knowledge transfer pipeline. Designing a Personal OS (aka personal knowledge base) is the easy part. The architecture only starts to pay off when you fill it for years -not just posts you liked, but decision patterns, career and life details, half-formed thoughts, writing, the messy stuff. Getting all of that into the right markdown file, at the right time, in the right shape is still the bottleneck. I built a Chrome extension (Feed2Context, details in the article) that grabs a post with my notes from my feed, drops it into the filesystem, and my agents synthesize and route it. I also built OpenHome assistant as a voice pipeline from my room into the wiki. Plus a bunch of MCP hooks into my accounts. But orchestrating all these helpers gets exhausting. A lot of people suggested Obsidian but I'm mostly on Readwise CLI to pull from X, LinkedIn, arXiv, books, and news. It works well on mobile, and because it’s a CLI, agents can find what they need and push it into the filesystem. Skill registries help a lot, in the videos I’ve got flows like Readwise CLI + alphaXiv MCP for research papers: save a paper, agent pulls the full text, analyzes it, teaches me back. I’m also testing Zapier CLI, and waiting for especially the Triggers API, between things like Yutori or plain cron, keeping a personal wiki alive is still hard; nobody wants to be the cron job for their own life, so triggers might be part of the answer. TL;DR: A personal filesystem you control isn’t optional if you don’t want to rent your memory from one AI company. The open problem is keeping it fed and current. What I actually want is one solution that can watch my screen, hear my voice, read my accounts, and write into my Personal OS without me acting as the integration layer forever.

Muratcan Koylan

76,272 просмотров • 4 месяцев назад

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

Sam Hogan 🇺🇸

374,653 просмотров • 3 лет назад

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!

Onur Solmaz

149,558 просмотров • 4 месяцев назад

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on mainstream cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

293,234 просмотров • 3 месяцев назад

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,497 просмотров • 4 месяцев назад

Episode 18 of the @TradingPlacesPod featuring James Riney from Coral Capital is out now! This week: Dave McClure and Aman Verjee kick off 2026 with their macroeconomic predictions (GDP growth, Fed rate cuts, and tax refunds), break down the IPO market outlook (SpaceX at $1.6T?!, OpenAI, Anthropic, Databricks), and dive deep into the secondary market dynamics for companies that won't go public in 2026. Plus, we explore the private Mag 7 vs. public Mag 7 in our [valuation corner] to answer the big question: where would you rather be—public or private markets? Then, Dave sits down with James Riney🐠Coral Capital, founder and managing partner of Coral Capital in Tokyo, Japan. James shares how he went from J.P. Morgan to founding one of Japan's largest crypto exchanges (Coin Check) to launching 500 Startups Japan (now Coral Capital) at age 26. They discuss Japan's explosive VC growth (from $700M to $7-10B annually), the rise of "hidden unicorns" (companies that IPO'd before hitting $1B), why secondaries weren't a thing in Japan until recently, and Coral's massive $100M secondary sale in SmartHR—the largest secondary exit ever in Japan that returned 6X on their fund while still holding half their stake. [ timestamps ] 00:00 – cold open 01:07 – [ tech & vc news ] 01:29 – macro growth picture 🖼️ 07:40 – 2026 IPO predictions 🔮 10:24 – 2ndry market game plan 🏈 16:43 – NVIDIA gives Groq Inc $20B 💰 22:00 – SoftBank buys DigitalBridge $4B 🌉 23:01 – Google acquires Intersect $4.75B 💸 25:42 – Meta hands Manus $2B 👋 27:25 – hot IPO Market in China 31:35 – [ intvw: James Riney / Coral Capital ] 31:46 – $100M 2ndry sale of SmartHR 🧠 34:44 – Japanese founding story 🏯 36:15 – Japan startup scene 🇯🇵 41:35 – Coral Capital strategy 🪸 43:54 – China vs Japan vs USA 🌏 1:13:59 – [ val corner: mag7 public vs private ] 1:13:59 – public mag7 analysis 🔔 1:18:38 – Tesla Elon Musk magic 🪄 1:22:00 – private mag7 deep dive 🕵️ 1:27:10 – 2026 2ndry strategy 🧐 1:30:51 – second takes Pod highlights this week: -2026 macro setup is strong: 4% GDP growth, Fed quantitative easing starting, and the biggest tax refunds ever hitting Q1 (tips, social security, overtime exemptions) -IPO predictions: SpaceX targeting $1.6T valuation, OpenAI and Anthropic likely to go public, plus Databricks, Stripe, Canva, Kraken in the mix -Private market reality check: top 10 AI companies getting all the attention, but the real secondary opportunities are in the next 200-300 companies trading at discounts -Nvidia's shopping spree: $20B for Groq (talent + inference tech), $100B in annual free cash flow to deploy—expect more acquisitions -Japan VC market exploded: from $700M (2015) to $7-10B today, with companies IPO'ing at series B/C stage instead of staying private for 12 years -SmartHR secondary: Coral Capital's $100M sale to General Atlantic = largest secondary in Japan history, 6X return on fund, still holding half their stake for the IPO -Hidden unicorns in Japan: 42+ companies hit $1B valuation within 12 years but were already public—so they don't count in "unicorn" stats (definition = private company) -Why secondaries are new in Japan: companies used to IPO at $60M revenue (series B/C stage), so no need for secondary liquidity—but now top companies raising $100M+ private rounds and staying private longer -Valuation corner showdown: Public Mag 7 trading at 7-10X revenue with profits; Private Mag 7 top tier (OpenAI, Anthropic, xAI) at 20-30X revenue with no profits—priced for perfection -Dave's take: avoid the froth at the top (OpenAI, Anthropic, SpaceX), hunt for value in second-tier private companies (Canva at 7X revenue, Databricks at 20X revenue) trading at discounts outside organized tender windows -James Riney's journey: from 26-year-old startup founder to launching 500 Startups Japan (now Coral Capital) to closing the largest secondary in Japanese VC history -US-Japan partnership thesis: Japan is the largest foreign investor in the US ($500B+), critical manufacturing ally in the China pullback, and Coral Capital now investing in US companies that can win Japan

Episode 18 of the @TradingPlacesPod featuring James Riney from Coral Capital is out now! This week: Dave McClure and Aman Verjee kick off 2026 with their macroeconomic predictions (GDP growth, Fed rate cuts, and tax refunds), break down the IPO market outlook (SpaceX at $1.6T?!, OpenAI, Anthropic, Databricks), and dive deep into the secondary market dynamics for companies that won't go public in 2026. Plus, we explore the private Mag 7 vs. public Mag 7 in our [valuation corner] to answer the big question: where would you rather be—public or private markets? Then, Dave sits down with James Riney🐠Coral Capital, founder and managing partner of Coral Capital in Tokyo, Japan. James shares how he went from J.P. Morgan to founding one of Japan's largest crypto exchanges (Coin Check) to launching 500 Startups Japan (now Coral Capital) at age 26. They discuss Japan's explosive VC growth (from $700M to $7-10B annually), the rise of "hidden unicorns" (companies that IPO'd before hitting $1B), why secondaries weren't a thing in Japan until recently, and Coral's massive $100M secondary sale in SmartHR—the largest secondary exit ever in Japan that returned 6X on their fund while still holding half their stake. [ timestamps ] 00:00 – cold open 01:07 – [ tech & vc news ] 01:29 – macro growth picture 🖼️ 07:40 – 2026 IPO predictions 🔮 10:24 – 2ndry market game plan 🏈 16:43 – NVIDIA gives Groq Inc $20B 💰 22:00 – SoftBank buys DigitalBridge $4B 🌉 23:01 – Google acquires Intersect $4.75B 💸 25:42 – Meta hands Manus $2B 👋 27:25 – hot IPO Market in China 31:35 – [ intvw: James Riney / Coral Capital ] 31:46 – $100M 2ndry sale of SmartHR 🧠 34:44 – Japanese founding story 🏯 36:15 – Japan startup scene 🇯🇵 41:35 – Coral Capital strategy 🪸 43:54 – China vs Japan vs USA 🌏 1:13:59 – [ val corner: mag7 public vs private ] 1:13:59 – public mag7 analysis 🔔 1:18:38 – Tesla Elon Musk magic 🪄 1:22:00 – private mag7 deep dive 🕵️ 1:27:10 – 2026 2ndry strategy 🧐 1:30:51 – second takes Pod highlights this week: -2026 macro setup is strong: 4% GDP growth, Fed quantitative easing starting, and the biggest tax refunds ever hitting Q1 (tips, social security, overtime exemptions) -IPO predictions: SpaceX targeting $1.6T valuation, OpenAI and Anthropic likely to go public, plus Databricks, Stripe, Canva, Kraken in the mix -Private market reality check: top 10 AI companies getting all the attention, but the real secondary opportunities are in the next 200-300 companies trading at discounts -Nvidia's shopping spree: $20B for Groq (talent + inference tech), $100B in annual free cash flow to deploy—expect more acquisitions -Japan VC market exploded: from $700M (2015) to $7-10B today, with companies IPO'ing at series B/C stage instead of staying private for 12 years -SmartHR secondary: Coral Capital's $100M sale to General Atlantic = largest secondary in Japan history, 6X return on fund, still holding half their stake for the IPO -Hidden unicorns in Japan: 42+ companies hit $1B valuation within 12 years but were already public—so they don't count in "unicorn" stats (definition = private company) -Why secondaries are new in Japan: companies used to IPO at $60M revenue (series B/C stage), so no need for secondary liquidity—but now top companies raising $100M+ private rounds and staying private longer -Valuation corner showdown: Public Mag 7 trading at 7-10X revenue with profits; Private Mag 7 top tier (OpenAI, Anthropic, xAI) at 20-30X revenue with no profits—priced for perfection -Dave's take: avoid the froth at the top (OpenAI, Anthropic, SpaceX), hunt for value in second-tier private companies (Canva at 7X revenue, Databricks at 20X revenue) trading at discounts outside organized tender windows -James Riney's journey: from 26-year-old startup founder to launching 500 Startups Japan (now Coral Capital) to closing the largest secondary in Japanese VC history -US-Japan partnership thesis: Japan is the largest foreign investor in the US ($500B+), critical manufacturing ally in the China pullback, and Coral Capital now investing in US companies that can win Japan

trading places

23,864 просмотров • 7 месяцев назад

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

JJ Englert

571,280 просмотров • 5 месяцев назад

This solo developer just built software that rewrites its own code. No team. No funding. No corporate backing. Just one guy, a stack of terminals, and an AI agent that decided to evolve itself. His name is Peter Steinberger. He built OpenClaw. The fastest-growing GitHub repo in HISTORY. And in a 3 hour Lex Fridman interview, he dropped details and predictions that should terrify the entire tech industry... Peter built a personal AI agent that lives on your computer. Talks to you through WhatsApp. Has access to your files, calendar, everything. Cool but not groundbreaking... Yet. Then one day he sent it a voice message. Problem: he never built voice support. No instructions. No code for audio. Nothing. The agent checked the file header, identified the format, converted it with ffmpeg, realized Whisper wasn't installed, found an OpenAI API key on the system, used Curl to send the file for transcription, and replied with the answer. Nobody taught it any of that. ZERO instructions. It just figured it out. But that's not even the scary part. The agent literally modifies its OWN source code. When something doesn't work, it rewrites itself. When Peter debugs, he doesn't read code. He asks the agent to read its OWN source code, find the bug, and fix itself. Self-modifying software. Built by accident. Running in production. Used by hundreds of thousands of people. And BOTH Mark Zuckerberg and Sam Altman are personally trying to recruit him. Not through HR or recruiters. Zuckerberg spent a WEEK playing with OpenClaw. Sending Peter direct messages. "This is great." "This is shit." "Fix this." When they first connected Peter asked to hop on a call and Mark said "Give me 10 minutes, I need to finish coding." But Sam Altman's playing a different game. There's an NDA involved but Peter hinted the Cerebras deal translates into speeds that feel like "being handed Thor's hammer." His exact words: "I've been lured with tokens." Two of the most powerful people in tech fighting over one open-source developer who built everything from his apartment. Peter's response to both: "I don't do this for the money. I don't give a fuck." But the real reason this matters isn't Peter's career. It's what this means for every app on your phone. Peter's prediction: 80% of apps are about to die. Why do you need MyFitnessPal when your agent knows where you are, what you ate, how you slept, and adjusts your workout based on your stress levels? Why do you need a calendar app when you tell your agent "remind me about dinner tomorrow and invite my friends" and it handles the entry, the WhatsApp messages, and the reservation in one shot? Why do you need Uber Eats when your agent opens a browser, places the order, and tracks delivery without you touching a screen? Every app becomes a slow API whether the company likes it or not. Companies like Cloudflare are trying to block agent access. But all they're doing is making things slower. Not impossible. Peter said it perfectly: "You're not taking a feature away. You're just making it take longer." The app economy is on borrowed time. Not because the apps are bad... Because one personal agent that knows you and can interact with any service makes most of them pointless. In the future, we'll have one agent that handles everything. Software that rewrites itself. Agents that learn without instructions. Two CEOs fighting over a solo dev. And an app economy about to get wrecked. This isn't coming in 5 years. It's literally already here. Most people just haven't installed it yet.

This solo developer just built software that rewrites its own code. No team. No funding. No corporate backing. Just one guy, a stack of terminals, and an AI agent that decided to evolve itself. His name is Peter Steinberger. He built OpenClaw. The fastest-growing GitHub repo in HISTORY. And in a 3 hour Lex Fridman interview, he dropped details and predictions that should terrify the entire tech industry... Peter built a personal AI agent that lives on your computer. Talks to you through WhatsApp. Has access to your files, calendar, everything. Cool but not groundbreaking... Yet. Then one day he sent it a voice message. Problem: he never built voice support. No instructions. No code for audio. Nothing. The agent checked the file header, identified the format, converted it with ffmpeg, realized Whisper wasn't installed, found an OpenAI API key on the system, used Curl to send the file for transcription, and replied with the answer. Nobody taught it any of that. ZERO instructions. It just figured it out. But that's not even the scary part. The agent literally modifies its OWN source code. When something doesn't work, it rewrites itself. When Peter debugs, he doesn't read code. He asks the agent to read its OWN source code, find the bug, and fix itself. Self-modifying software. Built by accident. Running in production. Used by hundreds of thousands of people. And BOTH Mark Zuckerberg and Sam Altman are personally trying to recruit him. Not through HR or recruiters. Zuckerberg spent a WEEK playing with OpenClaw. Sending Peter direct messages. "This is great." "This is shit." "Fix this." When they first connected Peter asked to hop on a call and Mark said "Give me 10 minutes, I need to finish coding." But Sam Altman's playing a different game. There's an NDA involved but Peter hinted the Cerebras deal translates into speeds that feel like "being handed Thor's hammer." His exact words: "I've been lured with tokens." Two of the most powerful people in tech fighting over one open-source developer who built everything from his apartment. Peter's response to both: "I don't do this for the money. I don't give a fuck." But the real reason this matters isn't Peter's career. It's what this means for every app on your phone. Peter's prediction: 80% of apps are about to die. Why do you need MyFitnessPal when your agent knows where you are, what you ate, how you slept, and adjusts your workout based on your stress levels? Why do you need a calendar app when you tell your agent "remind me about dinner tomorrow and invite my friends" and it handles the entry, the WhatsApp messages, and the reservation in one shot? Why do you need Uber Eats when your agent opens a browser, places the order, and tracks delivery without you touching a screen? Every app becomes a slow API whether the company likes it or not. Companies like Cloudflare are trying to block agent access. But all they're doing is making things slower. Not impossible. Peter said it perfectly: "You're not taking a feature away. You're just making it take longer." The app economy is on borrowed time. Not because the apps are bad... Because one personal agent that knows you and can interact with any service makes most of them pointless. In the future, we'll have one agent that handles everything. Software that rewrites itself. Agents that learn without instructions. Two CEOs fighting over a solo dev. And an app economy about to get wrecked. This isn't coming in 5 years. It's literally already here. Most people just haven't installed it yet.

Ricardo

85,846 просмотров • 6 месяцев назад

🎉 new skill unlocked: 20s uninterrupted, unstitched, single render from our new ai video engine: Nami. This is my birb (#7531) from the Moonbirds collection, idling in the library. patent: "Intra-Latent Semantic Injection via Cross-Spatial Encoding and Decoding during Multi-Pass Inference for Generative AI Video Creation" At Scrypted we've been quietly working on an agentic generative AI stack for two years: • integrating and testing w/ partners across the games & entertainment sectors • stealthily building a community of early believers through AVB • showcasing some of what we're doing with amazing projects like H011yw00d Agent. -- about Nami -- Nami is an agentic orchestration layer for AI video models: it unlocks their inner superpowers without making them rely on custom LoRAs or fine-tunings. Instead of throwing raw training power and tens of millions of dollars at training yet another ai video model: we figured out new ways to use what we have. Nami harnesses a multi-agent system to perform the work needed in taking a simple prompt or image and turning it into something bigger - much bigger. The agentic steps are allowed to manipulate latent space, digging into tensors, yet doing so in semantically aware chunks - meaning that Nami inherently supports video generation of arbitrary length, though it's bound to O(n) rendering time. (We do have some cool sharding tech that allows us to cut the generative time in half for a reference pose idle-animation like this demo). It's also fairly agnostic, picking and choosing the right tools for the job, and plays really well with emerging tech like FLUX Kontext, FramePack, or <- without being limited by any of them. -- use cases -- Even just a year or two ago the 20 second render below would cost a company, paying an agency, around $10k start-to-finish. This one cost me $6.25 on our dev hardware in an unoptimized environment. There's something mind-blowing about the state-of-the-art when we reduce costs to 0.0625% - less than 1% - of what we used to pay. It's also empowering. For creators. Game developers. Content influencers: you name it. -- superpowers -- 1. it does the things you ask for, in the order you asked for it 2. consistency is king 3. single-shot text or image-to-video 4. future videos can reference previous ones to seamlessly maintain style 5. semantic stitching: can't wait to showcase this -- gtm -- We think Generative AI Video, like image generation, like text, like games, should be a publicly accessible common good. We believe democratizing access to Nami in web3, via x402 payments proposed by Drew Coffman, or in World's mini-apps, is a bold step forward for digital freedom. Permissionless, decentralized, generative ai video. Naturally, we'll also soon release a web platform for using Nami in a traditionally SaaSy way: bring your own images, videos, or prompts and we'll take care of the rest. In the mid-term, Scrypted is building a stack of agentic skills (we call it AVB) and making them available to projects like H011yw00d Agent on Virtuals Protocol and other platforms. -- long-term vision -- Scrypted's mission is to decentralize the things that can't be decentralized. We participated in a16z crypto's CSX (London 2024) during our pre-seed specifically to research a new consensus protocol for hard things like AI video and AI agents: where there's no "one right answer". When Zero-Knowledge Proofs (ZKP) can't secure it, and Trusted Execution Environments (TEEs) are too small, we've got you covered with our upcoming Inori Network. -- how you can help -- 1. Are you a GPU farm? We're gonna need more flops. 2. Do you represent an L1 or L2? We want to build bridges. 3. Do you represent a Wallet or App creator? Let's get an endpoint exposed. 4. Are you an investor? Let's chat. 5. Like, repost, share! -- team background -- We come from a background of AI in the Video Game industry with each founder having over 20 years of experience at companies like Electronic Arts & Square Enix. -- contact -- DMs are open, reach out if you want to be an early tester for your site, game, collection, or project! -- try it out -- Go anywhere on X and tag H011yw00d Agent with a prompt and she'll give you a free 2 second render. Have fun making cinematic shorts or meme videos! -- thanks -- AWS Startups has been an incredible help scaling our prototypes. Also, shout out to all loyal beans 🫘 in the Autonomous Virtuals Beings (AVB) community. Nami has a very important role in the upcoming XP agent platform, can't wait to show you all. AVbeings

🎉 new skill unlocked: 20s uninterrupted, unstitched, single render from our new ai video engine: Nami. This is my birb (#7531) from the Moonbirds collection, idling in the library. patent: "Intra-Latent Semantic Injection via Cross-Spatial Encoding and Decoding during Multi-Pass Inference for Generative AI Video Creation" At Scrypted we've been quietly working on an agentic generative AI stack for two years: • integrating and testing w/ partners across the games & entertainment sectors • stealthily building a community of early believers through AVB • showcasing some of what we're doing with amazing projects like H011yw00d Agent. -- about Nami -- Nami is an agentic orchestration layer for AI video models: it unlocks their inner superpowers without making them rely on custom LoRAs or fine-tunings. Instead of throwing raw training power and tens of millions of dollars at training yet another ai video model: we figured out new ways to use what we have. Nami harnesses a multi-agent system to perform the work needed in taking a simple prompt or image and turning it into something bigger - much bigger. The agentic steps are allowed to manipulate latent space, digging into tensors, yet doing so in semantically aware chunks - meaning that Nami inherently supports video generation of arbitrary length, though it's bound to O(n) rendering time. (We do have some cool sharding tech that allows us to cut the generative time in half for a reference pose idle-animation like this demo). It's also fairly agnostic, picking and choosing the right tools for the job, and plays really well with emerging tech like FLUX Kontext, FramePack, or <- without being limited by any of them. -- use cases -- Even just a year or two ago the 20 second render below would cost a company, paying an agency, around $10k start-to-finish. This one cost me $6.25 on our dev hardware in an unoptimized environment. There's something mind-blowing about the state-of-the-art when we reduce costs to 0.0625% - less than 1% - of what we used to pay. It's also empowering. For creators. Game developers. Content influencers: you name it. -- superpowers -- 1. it does the things you ask for, in the order you asked for it 2. consistency is king 3. single-shot text or image-to-video 4. future videos can reference previous ones to seamlessly maintain style 5. semantic stitching: can't wait to showcase this -- gtm -- We think Generative AI Video, like image generation, like text, like games, should be a publicly accessible common good. We believe democratizing access to Nami in web3, via x402 payments proposed by Drew Coffman, or in World's mini-apps, is a bold step forward for digital freedom. Permissionless, decentralized, generative ai video. Naturally, we'll also soon release a web platform for using Nami in a traditionally SaaSy way: bring your own images, videos, or prompts and we'll take care of the rest. In the mid-term, Scrypted is building a stack of agentic skills (we call it AVB) and making them available to projects like H011yw00d Agent on Virtuals Protocol and other platforms. -- long-term vision -- Scrypted's mission is to decentralize the things that can't be decentralized. We participated in a16z crypto's CSX (London 2024) during our pre-seed specifically to research a new consensus protocol for hard things like AI video and AI agents: where there's no "one right answer". When Zero-Knowledge Proofs (ZKP) can't secure it, and Trusted Execution Environments (TEEs) are too small, we've got you covered with our upcoming Inori Network. -- how you can help -- 1. Are you a GPU farm? We're gonna need more flops. 2. Do you represent an L1 or L2? We want to build bridges. 3. Do you represent a Wallet or App creator? Let's get an endpoint exposed. 4. Are you an investor? Let's chat. 5. Like, repost, share! -- team background -- We come from a background of AI in the Video Game industry with each founder having over 20 years of experience at companies like Electronic Arts & Square Enix. -- contact -- DMs are open, reach out if you want to be an early tester for your site, game, collection, or project! -- try it out -- Go anywhere on X and tag H011yw00d Agent with a prompt and she'll give you a free 2 second render. Have fun making cinematic shorts or meme videos! -- thanks -- AWS Startups has been an incredible help scaling our prototypes. Also, shout out to all loyal beans 🫘 in the Autonomous Virtuals Beings (AVB) community. Nami has a very important role in the upcoming XP agent platform, can't wait to show you all. AVbeings

Tim Cotten

12,708 просмотров • 1 год назад

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 просмотров • 5 месяцев назад

Today's a special day for Lightspeed India. Introducing INDIA ASCENDS'2026, a program purpose-built for India's youngest (>25yo), boldest, cohort of world shapers & change makers. If you are one of them, put your headphones on, turn to volume to max, click on the video, and read on :) Building something is hard but building something the world has never seen before is nigh impossible. There is this concept of not just building a kingdom, but building a kingdom at the edge of a precipice -- founders who want to go all the way to the edge of what’s possible, beyond which there is no land, there is no road, the compass stops working, and they look into the abyss, and say ‘yes, this is for me, this will be my life’s work’. These are the rarest of birds that take the plunge and know they’d fall before they fly, but when they fly, oh how glorious do they look. We Lightspeed India have been fortunate to partner with several of these founders. We met Pixxel when the founders Awais Ahmed Kshitij Khandelwal were just 22yo. We backed Airbound when Naman Pushp was just 17. We’ve backed many others doing their life’s work at absolute cutting edge of what’s possible - Arun Vinayak of Exponent Energy, Pratyush Kumar & Vivek Raghavan of Sarvam Devdut Dalal & Xavi Laguarta at Mitti Labs and more. Beyond our portfolio, there is some amazing founders doing their life’s work - Pawan Chandana Skyroot Aerospace , Soham Sankaran PopVax 🇮🇳 , khushi Aspera, N A Gokul CynLr, Adrian Schmidt sarla aviation, Deepinder Goyal LAT Aerospace & Temple, Manu J Nair @EtherealXTech & many more. We need more of these founders coming out of India. Not just that, we need to fill gap that exists in this market which is in backing really young (<25yo) founders who are tinkering in school or college labs, or spending their weekends building, experimenting and failing fast, and are truly building globally competitive and de novo tech that, if it works, can have huge consequences in the world. To that end, we are proud to launch INDIA ASCENDS'2026 -- our flagship yearly program for the most cracked young builders in the country doing incredible cutting-edge research in robotics, quantum, space, energy, AI, bio or more. Our program applications open today and we’ll select 12-15 of the best, boldest ideas that we think has the potential to shape the future. We’ll bring them all to BLR for a 2-day program. Each participant will get ~$100K in support from our partners Anthropic Groq Inc , Google Cloud Amazon Web Services and we’ll also select 3-4 winners who will get venture funded to build their dream starting from $200K all the way to $3M and almost $500K of non-dilutive credits & grants from our partners. We look forward to seeing the boldest ideas you've been working on. Link to apply in the first comment:

Today's a special day for Lightspeed India. Introducing INDIA ASCENDS'2026, a program purpose-built for India's youngest (>25yo), boldest, cohort of world shapers & change makers. If you are one of them, put your headphones on, turn to volume to max, click on the video, and read on :) Building something is hard but building something the world has never seen before is nigh impossible. There is this concept of not just building a kingdom, but building a kingdom at the edge of a precipice -- founders who want to go all the way to the edge of what’s possible, beyond which there is no land, there is no road, the compass stops working, and they look into the abyss, and say ‘yes, this is for me, this will be my life’s work’. These are the rarest of birds that take the plunge and know they’d fall before they fly, but when they fly, oh how glorious do they look. We Lightspeed India have been fortunate to partner with several of these founders. We met Pixxel when the founders Awais Ahmed Kshitij Khandelwal were just 22yo. We backed Airbound when Naman Pushp was just 17. We’ve backed many others doing their life’s work at absolute cutting edge of what’s possible - Arun Vinayak of Exponent Energy, Pratyush Kumar & Vivek Raghavan of Sarvam Devdut Dalal & Xavi Laguarta at Mitti Labs and more. Beyond our portfolio, there is some amazing founders doing their life’s work - Pawan Chandana Skyroot Aerospace , Soham Sankaran PopVax 🇮🇳 , khushi Aspera, N A Gokul CynLr, Adrian Schmidt sarla aviation, Deepinder Goyal LAT Aerospace & Temple, Manu J Nair @EtherealXTech & many more. We need more of these founders coming out of India. Not just that, we need to fill gap that exists in this market which is in backing really young (<25yo) founders who are tinkering in school or college labs, or spending their weekends building, experimenting and failing fast, and are truly building globally competitive and de novo tech that, if it works, can have huge consequences in the world. To that end, we are proud to launch INDIA ASCENDS'2026 -- our flagship yearly program for the most cracked young builders in the country doing incredible cutting-edge research in robotics, quantum, space, energy, AI, bio or more. Our program applications open today and we’ll select 12-15 of the best, boldest ideas that we think has the potential to shape the future. We’ll bring them all to BLR for a 2-day program. Each participant will get ~$100K in support from our partners Anthropic Groq Inc , Google Cloud Amazon Web Services and we’ll also select 3-4 winners who will get venture funded to build their dream starting from $200K all the way to $3M and almost $500K of non-dilutive credits & grants from our partners. We look forward to seeing the boldest ideas you've been working on. Link to apply in the first comment:

Hemant Mohapatra

728,104 просмотров • 8 месяцев назад

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.

doodlifts

12,172 просмотров • 5 месяцев назад

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 просмотров • 9 месяцев назад