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We got 755 tokens per second! 🔥 That's OpenMed privacy-filter v2 (nemotron, MLX 8-bit) reading a 13,000-token clinical file and redacting every identifier as it streams by. 1,152 caught across 22 PII categories, on a Mac. Nothing left the machine.

Maziyar PANAHI

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152,105 views • 2 months ago •via X (Twitter)

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OpenHands crushed Codex by 2.3× on token efficiency! We gave both agents the same task on the same model (Qwen3.5 35B): build an 8-bit Space Invaders in 3 iterations (build, fix, polish). Output: • OpenHands: 219K tokens, • Codex: 513K tokens, OpenHands beats Codex in local running. The difference is in how they handle context. OpenHands reuses the unchanged data across every pass and only pays for new tokens, while Codex re-sends and re-counts it every iteration. OpenHands finished a bit slower, but on local runs, tokens spent are the real cost.

OpenHands crushed Codex by 2.3× on token efficiency! We gave both agents the same task on the same model (Qwen3.5 35B): build an 8-bit Space Invaders in 3 iterations (build, fix, polish). Output: • OpenHands: 219K tokens, • Codex: 513K tokens, OpenHands beats Codex in local running. The difference is in how they handle context. OpenHands reuses the unchanged data across every pass and only pays for new tokens, while Codex re-sends and re-counts it every iteration. OpenHands finished a bit slower, but on local runs, tokens spent are the real cost.

atomic.chat

29,696 views • 2 months ago

Qwen3.8-Flash-Next from Qwen has day-0 support in Atomic Agent! 125B main model, 51B N-gram embeddings, 6B activated per token and 62.5 on SWE-bench Pro. We gave it a folder and it checked every file to sort them by content. Total: 25 steps + 230K tokens ≈ $0.07

Qwen3.8-Flash-Next from Qwen has day-0 support in Atomic Agent! 125B main model, 51B N-gram embeddings, 6B activated per token and 62.5 on SWE-bench Pro. We gave it a folder and it checked every file to sort them by content. Total: 25 steps + 230K tokens ≈ $0.07

Atomic Agent

44,442 views • 24 days ago

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

Noisy

185,632 views • 3 months ago

Introducing Respan P-1, the world’s best privacy model. Every AI application handles sensitive data. Existing approaches to protecting it force a tradeoff between accuracy, latency, and cost. We built P-1 to eliminate that tradeoff. P-1 sets a new state of the art for detecting and redacting sensitive information - outperforming existing models across our benchmark while being fast enough to run on every request. Starting today, P-1 powers PII redaction across Respan.

Introducing Respan P-1, the world’s best privacy model. Every AI application handles sensitive data. Existing approaches to protecting it force a tradeoff between accuracy, latency, and cost. We built P-1 to eliminate that tradeoff. P-1 sets a new state of the art for detecting and redacting sensitive information - outperforming existing models across our benchmark while being fast enough to run on every request. Starting today, P-1 powers PII redaction across Respan.

Respan

73,842 views • 16 days ago

NVIDIA'S $30K H100 JUST GOT OUT-INFERENCED BY A $250 RYZEN. 18 TOKENS/SEC ON A 35B MODEL, $0.03 IN ELECTRICITY, $47B GONE FROM NVDA BY FRIDAY download qwen3 35b-a3b -> load in lm studio -> disable gpu entirely -> run on cpu + ram -> hit 18 tokens/sec on a 6-core ryzen -> pay nothing per token. that loop is why moe models just made every consumer cpu a viable ai host and nvidia's inference monopoly is quietly on borrowed time. qwen3 35b-a3b + lm studio + ryzen 5 9600x + 32gb ddr5 + zero gpu - that's the stack. watch and save it, then run a 35b model on your laptop this weekend.

NVIDIA'S $30K H100 JUST GOT OUT-INFERENCED BY A $250 RYZEN. 18 TOKENS/SEC ON A 35B MODEL, $0.03 IN ELECTRICITY, $47B GONE FROM NVDA BY FRIDAY download qwen3 35b-a3b -> load in lm studio -> disable gpu entirely -> run on cpu + ram -> hit 18 tokens/sec on a 6-core ryzen -> pay nothing per token. that loop is why moe models just made every consumer cpu a viable ai host and nvidia's inference monopoly is quietly on borrowed time. qwen3 35b-a3b + lm studio + ryzen 5 9600x + 32gb ddr5 + zero gpu - that's the stack. watch and save it, then run a 35b model on your laptop this weekend.

Antid

23,745 views • 1 month ago

TWO BOXES THE SIZE OF A MAC MINI JUST RAN A 235 BILLION PARAMETER MODEL ON A DESK It is two NVIDIA DGX Spark units linked by a single cable. A year ago a model this size meant renting a GPU cluster by the hour. Now it sits next to your monitor for around $8,000. Here is the twist most people miss. Linking them does not create one shared 256GB memory pool. The model is split across both boxes, and that is the only reason a 235B model fits at all. It answers at roughly 10 tokens per second, and both chips sit at just 74 degrees while sipping around 50 watts. Every token stays on the desk. Nothing touches a cloud, and nothing leaves the room. The ceiling for what you can run at home just jumped from 70B to 235B. Bookmark this & Watch it run ↓

TWO BOXES THE SIZE OF A MAC MINI JUST RAN A 235 BILLION PARAMETER MODEL ON A DESK It is two NVIDIA DGX Spark units linked by a single cable. A year ago a model this size meant renting a GPU cluster by the hour. Now it sits next to your monitor for around $8,000. Here is the twist most people miss. Linking them does not create one shared 256GB memory pool. The model is split across both boxes, and that is the only reason a 235B model fits at all. It answers at roughly 10 tokens per second, and both chips sit at just 74 degrees while sipping around 50 watts. Every token stays on the desk. Nothing touches a cloud, and nothing leaves the room. The ceiling for what you can run at home just jumped from 70B to 235B. Bookmark this & Watch it run ↓

slash1s

101,317 views • 3 months ago

10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

10x more tokens/second per megawatt as measured by CoreWeave on NVIDIA Vera Rubin platform. That performance reflects a bigger shift in AI infrastructure economics: building AI factories that efficiently turn compute into tokens, and tokens into revenue. It takes extreme co-design across the full stack, combined with an open ecosystem built to innovate and scale. Dion Harris connects the economics to the technology with 5 critical questions every AI infrastructure leader should be asking across compute, networking, storage, software and security. ➡️

NVIDIA AI Infrastructure

23,895 views • 1 month ago

AI at Meta's Muse Glimmer (30B dense, open-weights) launches with SGLang day-0 support. We got ~230 tok/s on a single RTX 5090, with NVFP4 + DFlash on. It also works out of the box on NVIDIA AI Developer RTX Pro 6000, DGX Spark, and MLX for Mac. Speed and reliability have always been the blockers for a local agent. SGLang and Muse Glimmer make a local agent that's blazing fast, always on, and gets things done. Go build and run a local agent on your own machine!🚀

AI at Meta's Muse Glimmer (30B dense, open-weights) launches with SGLang day-0 support. We got ~230 tok/s on a single RTX 5090, with NVFP4 + DFlash on. It also works out of the box on NVIDIA AI Developer RTX Pro 6000, DGX Spark, and MLX for Mac. Speed and reliability have always been the blockers for a local agent. SGLang and Muse Glimmer make a local agent that's blazing fast, always on, and gets things done. Go build and run a local agent on your own machine!🚀

LMSYS Org

43,545 views • 1 month ago

ACTFUN V2 is live on Arc tesnet A complete redesign and the most ambitious version of the platform we've shipped yet. 🧵 Let break down exactly what ACTFUN is, how it works, and why V2 is a big deal. ACTFUN is a Mine-to-Launch token launchpad built on Arc testnet. The idea is simple: anyone can create a token. But instead of a presale or a bonding curve you buy into the community mines it by writing funny posts. No VCs. No insider allocations. Just the community earning supply by participating. Every token launched on ACTFUN has a fixed tokenomic structure: 5% mints instantly to the creator's wallet on launch, 60% is community-mineable (write a funny post, pay a small USDC fee, earn tokens), and 35% is locked as LP reserve. When the full 60% is mined out, the contract auto-graduates no manual step, no admin key, no intervention. Pure code. On graduation, ACTFUN seeds liquidity on up to 4 AMMs simultaneously: UNITFLOW V3 (a Uniswap V3 fork), Uniswap V2, Curve Finance (StableSwap), and Synthra V3 DEX. The 35% LP reserve + all accumulated USDC becomes real onchain liquidity the moment the last token is mined. That's the whole mechanism mine it, graduate it, trade it. V2 also brought a full visual overhaul. New hub landing with dedicated product slabs, Cinzel typography across the entire site, a cleaner Minepad grid, and ecosystem logos representing the infra stack powering the platform: Arc Goldsky ☀️ , Uniswap, Curve, Synthra, Neon, LiveKit, OpenZeppelin, Hardhat. Built to look as serious as the contracts underneath it. The infra running underneath ACTFUN V2: smart contracts in Solidity 0.8.24 with OpenZeppelin 5.x deployed on Arc Testnet (Chain ID 5042002), event indexing via @goldsky_io Turbo pipeline streaming into Neon Postgres with sub-1s lag and zero RPC fanout on the browser, live voice rooms via starnews2.com per token (creators broadcast video + mic, miners join by voice), and a React 18 + Vite + wagmi v2 + Dynamic Labs frontend. Everything is live on Arc Testnet right now. Mint testnet USDC, deploy a token, mine it with your community, and watch it graduate to a live DEX all onchain, all open,

ACTFUN V2 is live on Arc tesnet A complete redesign and the most ambitious version of the platform we've shipped yet. 🧵 Let break down exactly what ACTFUN is, how it works, and why V2 is a big deal. ACTFUN is a Mine-to-Launch token launchpad built on Arc testnet. The idea is simple: anyone can create a token. But instead of a presale or a bonding curve you buy into the community mines it by writing funny posts. No VCs. No insider allocations. Just the community earning supply by participating. Every token launched on ACTFUN has a fixed tokenomic structure: 5% mints instantly to the creator's wallet on launch, 60% is community-mineable (write a funny post, pay a small USDC fee, earn tokens), and 35% is locked as LP reserve. When the full 60% is mined out, the contract auto-graduates no manual step, no admin key, no intervention. Pure code. On graduation, ACTFUN seeds liquidity on up to 4 AMMs simultaneously: UNITFLOW V3 (a Uniswap V3 fork), Uniswap V2, Curve Finance (StableSwap), and Synthra V3 DEX. The 35% LP reserve + all accumulated USDC becomes real onchain liquidity the moment the last token is mined. That's the whole mechanism mine it, graduate it, trade it. V2 also brought a full visual overhaul. New hub landing with dedicated product slabs, Cinzel typography across the entire site, a cleaner Minepad grid, and ecosystem logos representing the infra stack powering the platform: Arc Goldsky ☀️ , Uniswap, Curve, Synthra, Neon, LiveKit, OpenZeppelin, Hardhat. Built to look as serious as the contracts underneath it. The infra running underneath ACTFUN V2: smart contracts in Solidity 0.8.24 with OpenZeppelin 5.x deployed on Arc Testnet (Chain ID 5042002), event indexing via @goldsky_io Turbo pipeline streaming into Neon Postgres with sub-1s lag and zero RPC fanout on the browser, live voice rooms via starnews2.com per token (creators broadcast video + mic, miners join by voice), and a React 18 + Vite + wagmi v2 + Dynamic Labs frontend. Everything is live on Arc Testnet right now. Mint testnet USDC, deploy a token, mine it with your community, and watch it graduate to a live DEX all onchain, all open,

ACT FUN

22,327 views • 2 months ago

$Anthropic went from a $9 billion to a $47 billion revenue run rate in five months, and almost every dollar is people paying by the token. That's the whole model: you're billed for what you feed in. RAG's answer is to feed in everything, every query. Twenty chunks retrieved. Nineteen barely related. You pay full frontier rate on all twenty. And it still misses the questions that live across three documents. Expensive and wrong at once. A graph flips the bill. Extract the edges once, up front. Then every query walks a path instead of re-reading the pile. You stop shipping twenty chunks to the model. You ship the three nodes that actually answer. Same question, a fraction of the tokens, and it finally connects facts that share no words. The vendor whose valuation just cleared $965 billion is quietly counting on you never building this. Pay-per-token only prints money while you retrieve like it's free. Don't let this sit in your bookmarks. Save it, then open your last invoice, find your worst tokens-per-query endpoint, and rebuild it as a graph walk tonight.$

Anthropic went from a $9 billion to a $47 billion revenue run rate in five months, and almost every dollar is people paying by the token. That's the whole model: you're billed for what you feed in. RAG's answer is to feed in everything, every query. Twenty chunks retrieved. Nineteen barely related. You pay full frontier rate on all twenty. And it still misses the questions that live across three documents. Expensive and wrong at once. A graph flips the bill. Extract the edges once, up front. Then every query walks a path instead of re-reading the pile. You stop shipping twenty chunks to the model. You ship the three nodes that actually answer. Same question, a fraction of the tokens, and it finally connects facts that share no words. The vendor whose valuation just cleared $965 billion is quietly counting on you never building this. Pay-per-token only prints money while you retrieve like it's free. Don't let this sit in your bookmarks. Save it, then open your last invoice, find your worst tokens-per-query endpoint, and rebuild it as a graph walk tonight.

wast3

12,707 views • 1 month ago

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

Noisy

48,763 views • 2 months ago

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours

Noisy

79,665 views • 3 months ago

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

THE $4,000 DGX SPARK BENCHMARKS AT 342 T/S ON PROMPT PROCESSING - THEN DROPS TO 73 WHEN IT ACTUALLY GENERATES. that clip is a dgx spark running llama-bench on qwen3-coder 30b, side by side with a mac. two numbers decide everything here: pp512, prompt processing = 342 tokens/sec tg128, token generation = 73 tokens/sec that gap is the whole lesson. local ai isn't one speed, it's two. prompt processing is reading your input: your files, your long context. the spark is compute-heavy, so it eats that fast. token generation is writing the answer, and it's bound by memory bandwidth, where the spark is modest and unified-memory macs stay close. so "which box is faster" has no single winner. feeding huge context or running agents that read a lot? prompt processing wins, the spark shines. long chat replies, one token at a time? generation matters more, and a mac holds its own. no single benchmark number, no universal "fastest box", no upgrade that wins every workload. the uncomfortable part isn't that the spark is fast. it's that "tokens per second" was always two numbers, and the hype only ever quotes the flattering one. follow RetroChainer for the benchmark truth nobody screenshots, and bookmark this before you buy on one number.

RetroChainer

42,451 views • 1 month ago

A developer in Hangzhou runs an AI that remembers everything about him for $0.40 a year. No vector database. One file that never grows past 4,000 tokens. He published the whole schema. His version starts from the opposite idea. Memory is not storage. It's a write policy. Six fields. Rewritten every time, never appended: > IDENTITY - who you are, what you build. 300 tokens. Changes monthly at most > STATE - what you're on right now. 400 tokens. Rewritten daily > DECISIONS - what's already settled, so nothing gets re-argued. 800 tokens > CORRECTIONS - every time you said "no, not like that." 600 tokens > PEOPLE - names, roles, who's waiting on what. 500 tokens > DEAD - tried and abandoned, so it never comes back as a suggestion. 400 tokens Three thousand tokens. Ceiling of four. When a section fills, the model rewrites it shorter. Nothing is ever added. Only replaced. Kimi K2.5 bills $0.10 per million cached input tokens. Four thousand tokens a turn is $0.0004. That's 2,500 turns for a dollar. The free tier hands you 1.5 million tokens a day. 375 turns before you pay anything at all. CORRECTIONS is the field nobody builds, and it's the one that does the work. A model that remembers being wrong stops repeating it. Everyone else is paying to search their own history. He pays to keep it short. The bill stopped growing when the file did. Your memory system isn't defined by what it stores. It's defined by what it agrees to delete. The article below is the full build - schema, rewrite prompts, the compaction rule that keeps it under the cap. Save it. You'll want it open in the other tab.

A developer in Hangzhou runs an AI that remembers everything about him for $0.40 a year. No vector database. One file that never grows past 4,000 tokens. He published the whole schema. His version starts from the opposite idea. Memory is not storage. It's a write policy. Six fields. Rewritten every time, never appended: > IDENTITY - who you are, what you build. 300 tokens. Changes monthly at most > STATE - what you're on right now. 400 tokens. Rewritten daily > DECISIONS - what's already settled, so nothing gets re-argued. 800 tokens > CORRECTIONS - every time you said "no, not like that." 600 tokens > PEOPLE - names, roles, who's waiting on what. 500 tokens > DEAD - tried and abandoned, so it never comes back as a suggestion. 400 tokens Three thousand tokens. Ceiling of four. When a section fills, the model rewrites it shorter. Nothing is ever added. Only replaced. Kimi K2.5 bills $0.10 per million cached input tokens. Four thousand tokens a turn is $0.0004. That's 2,500 turns for a dollar. The free tier hands you 1.5 million tokens a day. 375 turns before you pay anything at all. CORRECTIONS is the field nobody builds, and it's the one that does the work. A model that remembers being wrong stops repeating it. Everyone else is paying to search their own history. He pays to keep it short. The bill stopped growing when the file did. Your memory system isn't defined by what it stores. It's defined by what it agrees to delete. The article below is the full build - schema, rewrite prompts, the compaction rule that keeps it under the cap. Save it. You'll want it open in the other tab.

wast3

15,862 views • 28 days ago

Crypto isn't about the shitty coin you bought. That's the mistake many people make… lose money on a token, decide the whole thing is a scam, and walk away angry. All while the story keeps playing out around them. Blockchain is the coordination layer for the digital economy. It's how AI agents make payments at machine speed. It's how identity, privacy, and value move across the internet when humans aren't the primary users anymore. AI and blockchain are married at the hip. Every agent, autonomous transaction, piece of the machine economy that's arriving now needs a programmable layer to run on. That's what the layer 1s actually are. Own a piece of it, and you own a piece of the agent economy.

Crypto isn't about the shitty coin you bought. That's the mistake many people make… lose money on a token, decide the whole thing is a scam, and walk away angry. All while the story keeps playing out around them. Blockchain is the coordination layer for the digital economy. It's how AI agents make payments at machine speed. It's how identity, privacy, and value move across the internet when humans aren't the primary users anymore. AI and blockchain are married at the hip. Every agent, autonomous transaction, piece of the machine economy that's arriving now needs a programmable layer to run on. That's what the layer 1s actually are. Own a piece of it, and you own a piece of the agent economy.

Raoul Pal

111,174 views • 1 month ago

A Stanford 26-year-old spent 2 years turning 11,400 Obsidian notes into a neural net that finishes his sentences. It started as a folder called “misc” with 43 PDF in it. Most vaults die around 300 notes, because nothing connects and nothing comes back roughly 90% of what you save gets read once, on the day you save it. He fixed it by treating retrieval as the product and storage as the leftovers. Phase 1: Atomize (months 1-3) One idea per note, 200 words max, title written as a claim instead of a topic. “Attention is a filter, not a spotlight” beats “Notes on attention.” He rewrote 1,200 old notes this way and the vault shrank from 2,800 files to 1,600 usable ones. Phase 2: Embed locally (months 4-8) Ollama running nomic-embed-text on a Mac Mini M4, every note chunked at 512 tokens with 64-token overlap, all of it dumped into a local ChromaDB. 11,400 notes became 38,000 vectors and 1.2 GB on disk. Query time 0.4 seconds, cost $0 a month, nothing leaving the machine. Phase 3: Kill the hallucinations (months 9-14) Plain cosine search returned garbage about 30% of the time, so he bolted on a re-ranker and one hard rule: every retrieved chunk carries its source note title into the answer, and no citation means no answer. Wrong recalls fell from 1 in 4 to 1 in 30. Phase 4: Make it speak first (months 15-24) A nightly job reads the day’s writing, pulls the 5 oldest notes with the highest similarity, and drops 3 questions into tomorrow’s daily note. He stopped searching the vault somewhere in month 17 now it opens the conversation before he does. Before: 2,800 notes, 0 reused, 6 weeks per paper draft. After: 11,400 notes, 60-70 surfaced every week unasked, 9 days per draft. The whole stack is 4 pieces Obsidian, Ollama, ChromaDB, and a 40-line Python script that runs at 2 AM. Total software spend across 24 months: $0. Most people build a second brain to store things they will never open again. He stopped writing to remember. He writes so the machine can remind him.

A Stanford 26-year-old spent 2 years turning 11,400 Obsidian notes into a neural net that finishes his sentences. It started as a folder called “misc” with 43 PDF in it. Most vaults die around 300 notes, because nothing connects and nothing comes back roughly 90% of what you save gets read once, on the day you save it. He fixed it by treating retrieval as the product and storage as the leftovers. Phase 1: Atomize (months 1-3) One idea per note, 200 words max, title written as a claim instead of a topic. “Attention is a filter, not a spotlight” beats “Notes on attention.” He rewrote 1,200 old notes this way and the vault shrank from 2,800 files to 1,600 usable ones. Phase 2: Embed locally (months 4-8) Ollama running nomic-embed-text on a Mac Mini M4, every note chunked at 512 tokens with 64-token overlap, all of it dumped into a local ChromaDB. 11,400 notes became 38,000 vectors and 1.2 GB on disk. Query time 0.4 seconds, cost $0 a month, nothing leaving the machine. Phase 3: Kill the hallucinations (months 9-14) Plain cosine search returned garbage about 30% of the time, so he bolted on a re-ranker and one hard rule: every retrieved chunk carries its source note title into the answer, and no citation means no answer. Wrong recalls fell from 1 in 4 to 1 in 30. Phase 4: Make it speak first (months 15-24) A nightly job reads the day’s writing, pulls the 5 oldest notes with the highest similarity, and drops 3 questions into tomorrow’s daily note. He stopped searching the vault somewhere in month 17 now it opens the conversation before he does. Before: 2,800 notes, 0 reused, 6 weeks per paper draft. After: 11,400 notes, 60-70 surfaced every week unasked, 9 days per draft. The whole stack is 4 pieces Obsidian, Ollama, ChromaDB, and a 40-line Python script that runs at 2 AM. Total software spend across 24 months: $0. Most people build a second brain to store things they will never open again. He stopped writing to remember. He writes so the machine can remind him.

West Lord

170,060 views • 1 month ago

Nvidia CEO Jensen Huang on why engineers will soon be paid in tokens, not just salary: Jensen lays out a future where compute access becomes part of an engineer's compensation package. "I could totally imagine in the future every single engineer in our company will need an annual token budget," he says. He explains how the math would work: "They're going to make a few hundred thousand a year their base pay. I'm going to give them probably half of that on top of it as tokens so that they could be amplified 10x. Of course, we would." According to Huang, this is already changing how companies compete for talent: "It is now one of the recruiting tools in Silicon Valley. How many tokens comes along with my job?" His reasoning is simple: tokens make engineers more productive. As he puts it, "every engineer that has access to tokens will be more productive and those tokens as you know will be produced by AI factories that all of you and us we partner to build." Huang then zooms out to describe how this reshapes the nature of companies themselves: "Every single enterprise company in today sit on top of file systems and data centers. Every single software company of the future will be agentic and they will be token manufacturers. They be token users for their engineers and they'll be token manufacturers for all of their customers."

Nvidia CEO Jensen Huang on why engineers will soon be paid in tokens, not just salary: Jensen lays out a future where compute access becomes part of an engineer's compensation package. "I could totally imagine in the future every single engineer in our company will need an annual token budget," he says. He explains how the math would work: "They're going to make a few hundred thousand a year their base pay. I'm going to give them probably half of that on top of it as tokens so that they could be amplified 10x. Of course, we would." According to Huang, this is already changing how companies compete for talent: "It is now one of the recruiting tools in Silicon Valley. How many tokens comes along with my job?" His reasoning is simple: tokens make engineers more productive. As he puts it, "every engineer that has access to tokens will be more productive and those tokens as you know will be produced by AI factories that all of you and us we partner to build." Huang then zooms out to describe how this reshapes the nature of companies themselves: "Every single enterprise company in today sit on top of file systems and data centers. Every single software company of the future will be agentic and they will be token manufacturers. They be token users for their engineers and they'll be token manufacturers for all of their customers."

Big Brain AI

90,171 views • 4 months ago

Bari Weiss is recycling the same propaganda that's excused every privacy violation in history. Take a Colorado woman recently visited by a police sergeant over a theft accusation—his only "evidence" was a Flock camera hit. She was innocent. His words: "You can't get a breath of fresh air in or out of that place without us knowing." Sure, a CCP-style blanket surveillance and reading everyone's texts would catch more criminals. It would also end privacy as we know it. But in America we believe that liberty is more important than security.

Bari Weiss is recycling the same propaganda that's excused every privacy violation in history. Take a Colorado woman recently visited by a police sergeant over a theft accusation—his only "evidence" was a Flock camera hit. She was innocent. His words: "You can't get a breath of fresh air in or out of that place without us knowing." Sure, a CCP-style blanket surveillance and reading everyone's texts would catch more criminals. It would also end privacy as we know it. But in America we believe that liberty is more important than security.

Joshua Reed Eakle 🗽

41,206 views • 1 month ago

TOKENFI TOKEN LAUNCHER IS LIVE ON MAINNET IT’S OFFICIAL! TokenFi Token Launcher is live on the mainnet of five top EVM blockchains: Ethereum, BNB Chain, Base, Arbitrum, and opBNB. With TokenFi Token Launcher, ANYONE can create a fungible crypto token (ERC-20), NFT (ERC-721), or Multi-Token (ERC-1155) in just a few clicks, regardless of their crypto experience. The TokenFi platform and framework has been thoroughly audited and is being monitored in real-time by industry-leading blockchain security auditor Certik. This means that the tokens you create with TokenFi are safe and audited. TokenFi Token Launcher is powered by $TOKEN as its main utility token, and every successful transaction on the Ethereum and BNB blockchains buys and burns $TOKEN, enhancing its utility and making it perpetually deflationary as adoption of the platform grows. TokenFi Token Launcher aims to be the dominant token creation platform on EVM blockchains, and to this effect, we will be rolling out the platform on dozens of additional EVM blockchains in the coming weeks and months. You can learn more about TokenFi Token Launcher here:

TOKENFI TOKEN LAUNCHER IS LIVE ON MAINNET IT’S OFFICIAL! TokenFi Token Launcher is live on the mainnet of five top EVM blockchains: Ethereum, BNB Chain, Base, Arbitrum, and opBNB. With TokenFi Token Launcher, ANYONE can create a fungible crypto token (ERC-20), NFT (ERC-721), or Multi-Token (ERC-1155) in just a few clicks, regardless of their crypto experience. The TokenFi platform and framework has been thoroughly audited and is being monitored in real-time by industry-leading blockchain security auditor Certik. This means that the tokens you create with TokenFi are safe and audited. TokenFi Token Launcher is powered by $TOKEN as its main utility token, and every successful transaction on the Ethereum and BNB blockchains buys and burns $TOKEN, enhancing its utility and making it perpetually deflationary as adoption of the platform grows. TokenFi Token Launcher aims to be the dominant token creation platform on EVM blockchains, and to this effect, we will be rolling out the platform on dozens of additional EVM blockchains in the coming weeks and months. You can learn more about TokenFi Token Launcher here:

TokenFi

1,289,000 views • 2 years ago

🔥 NVIDIA GTC 2026: The Vera Rubin platform just dropped, introducing 10x inference throughput per watt, at 1/10th the cost per token vs. Blackwell. Jensen Huang called it: every company will now run a "token factory." Here's the thing: Aethir has been running distributed token factories since day one, with nearly 440,000+ GPU containers across 94 countries, delivering 1.5B+ compute hours to enterprises, powered by ATH. When the world's most powerful AI chips land, you need a cloud that can actually deploy them at scale, without the centralized cloud limitations and bottlenecks. That's Aethir’s advantage over centralized compute providers. ⚡

🔥 NVIDIA GTC 2026: The Vera Rubin platform just dropped, introducing 10x inference throughput per watt, at 1/10th the cost per token vs. Blackwell. Jensen Huang called it: every company will now run a "token factory." Here's the thing: Aethir has been running distributed token factories since day one, with nearly 440,000+ GPU containers across 94 countries, delivering 1.5B+ compute hours to enterprises, powered by ATH. When the world's most powerful AI chips land, you need a cloud that can actually deploy them at scale, without the centralized cloud limitations and bottlenecks. That's Aethir’s advantage over centralized compute providers. ⚡

Aethir Ecosystem

15,470 views • 6 months ago