Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Introducing WORM TV 🪱 fully autonomous intergalactic news channel covering the Singularity with zero human bias TODAY: Anthropic watermarks 🚿, DeepSeek V4 Pro 🐳

wormtelevision's profile picture

🪱 WORM TV

1,627 subscribers

112,585 görüntüleme • 4 gün önce •via X (Twitter)

Bilim & TeknolojiHaberler & Politika
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

FREE DeepSeek V4 Pro and V4 Flash API. Runtime by Bad Theory Labs just dropped it. Same platform, new free model, same 2-minute setup. > Runtime already gave you free Claude Opus 4.8 and GPT 5.5 access. Now they added DeepSeek V4 Pro and V4 Flash to the free tier. Absolutely FREE, YES. Same exact flow as before: → Go to runtime(.)badtheorylabs(.)com → Sign up with Google → Fill in your details during onboarding → Free balance lands automatically(But you don't need it to use DeepSeek - it's completely free right now) → Grab your API key from the dashboard → Plug into OpenCode, OpenClaw, or Hermes - done DeepSeek V4 Pro is one of the strongest coding and reasoning models available right now. Free tier also includes btl-2 - auto-routes between GLM, OpenAI, Anthropic, and Gemma. 10 million tokens per month at zero cost. 344 models total. One OpenAI-compatible endpoint. No code rebuild, no card, no limits hunting. >> Just start using the API, and when choosing a provider, select DeepSeek I tested it myself - fully working. Bookmark this before the free tier fills up.
0:23

FREE DeepSeek V4 Pro and V4 Flash API. Runtime by Bad Theory Labs just dropped it. Same platform, new free model, same 2-minute setup. > Runtime already gave you free Claude Opus 4.8 and GPT 5.5 access. Now they added DeepSeek V4 Pro and V4 Flash to the free tier. Absolutely FREE, YES. Same exact flow as before: → Go to runtime(.)badtheorylabs(.)com → Sign up with Google → Fill in your details during onboarding → Free balance lands automatically(But you don't need it to use DeepSeek - it's completely free right now) → Grab your API key from the dashboard → Plug into OpenCode, OpenClaw, or Hermes - done DeepSeek V4 Pro is one of the strongest coding and reasoning models available right now. Free tier also includes btl-2 - auto-routes between GLM, OpenAI, Anthropic, and Gemma. 10 million tokens per month at zero cost. 344 models total. One OpenAI-compatible endpoint. No code rebuild, no card, no limits hunting. >> Just start using the API, and when choosing a provider, select DeepSeek I tested it myself - fully working. Bookmark this before the free tier fills up.

Atenov int.

26,920 görüntüleme • 1 ay önce

Introducing Faceless Clipping Channel, a fully autonomous company run by agents on Naive. One prompt → complete team of agents running your own faceless short-form empire: > Research your niche + current trending topics daily > Scan long-form content for high-ROI clips > Extract and score the most likely viral-worthy snippets > Edit them with perfect formatting for TikTok, Reels & Shorts > Recursively learn from analytics to scale virality Zero filming. Zero editing. Zero employees.
0:32

Introducing Faceless Clipping Channel, a fully autonomous company run by agents on Naive. One prompt → complete team of agents running your own faceless short-form empire: > Research your niche + current trending topics daily > Scan long-form content for high-ROI clips > Extract and score the most likely viral-worthy snippets > Edit them with perfect formatting for TikTok, Reels & Shorts > Recursively learn from analytics to scale virality Zero filming. Zero editing. Zero employees.

naïve

28,709 görüntüleme • 2 ay önce

Big news, Claude just got a major upgrade today which I'm happy to introduce in shipper. From today on, Claude Opus 4.6 is able to build and run a full-scale business for you. Anthropic keeps launching new models, so we've used them all to come up with the ultimate AI company builder. Introducing Shipper 2.0, AI that does everything for you. - Searches working strategies - Thinks, designs, codes, launches & monetizes your app - Keeps running it for you Fully in your browser. Builds stores, websites, mobile apps, web apps, extensions, and more. Try it out @
1:35

Big news, Claude just got a major upgrade today which I'm happy to introduce in shipper. From today on, Claude Opus 4.6 is able to build and run a full-scale business for you. Anthropic keeps launching new models, so we've used them all to come up with the ultimate AI company builder. Introducing Shipper 2.0, AI that does everything for you. - Searches working strategies - Thinks, designs, codes, launches & monetizes your app - Keeps running it for you Fully in your browser. Builds stores, websites, mobile apps, web apps, extensions, and more. Try it out @

David Ch

210,426 görüntüleme • 5 ay önce

hy3 vs mimo-v2.5 vs deepseek v4 flash vs minimax m3 the four models on top of the openrouter leaderboard by tokens this week: #1 hy3 (Tencent Hy) – 7.5t #2 mimo-v2.5 (Xiaomi MiMo) – 6.56t #3 deepseek v4 flash (DeepSeek) – 5.24t #4 minimax m3 (MiniMax (official)) – 4.21t so we tested them. 3 prompts, single-file html, Three.js from a cdn, fully procedural, no external assets. all run via AI/ML API each prompt is a transparent cutaway machine that has to be mechanically correct, not decorative: • 4-stroke engine with full oil circulation – slider-crank kinematics, cam at 2:1, valve lift driven by lobes, oil loop from sump to gallery to big-end • watt walking-beam steam engine – four-bar vector-loop closure, eccentric-driven slide valve, steam events synced to real port position • francis reaction water turbine – 20 guide vanes on a regulating ring, 17 lofted runner blades, gpu particle advection, precessing vortex rope at part load the takeaway up front: none of the four cleared all three scenes on the first attempt. but the price spread between them is roughly 70x – hy3 fixed included costs less than two cents overall results (summed across all 3 scenes): cost #1 hy3 – $0.016 #2 deepseek v4 flash – $0.025 #3 mimo-v2.5 – $0.97 #4 minimax m3 – $1.17 tokens #1 hy3 – 19,326 #2 deepseek v4 flash – 63,126 #3 mimo-v2.5 – 322,523 #4 minimax m3 – 702,900 lines of code #1 hy3 – 1,047 #2 mimo-v2.5 – 2,759 #3 deepseek v4 flash – 3,273 #4 minimax m3 – 3,354 scenes needing a second attempt #1 hy3 – 1 (engine) #1 mimo-v2.5 – 1 (turbine) #1 minimax m3 – 1 (turbine) #4 deepseek v4 flash – 2 (steam engine, turbine) observations: 1. the token spread is the real story – minimax burns 36x hy3's tokens and lands in the same place, one retry, ~3.3k lines 2. hy3 is the outlier on density: 1,047 lines total, fewest tokens, cheapest run, and only one scene needed a second pass. deepseek is the opposite trade – near-hy3 pricing but the most retries 3. mimo and minimax seem to overthink instead of writing the code. minimax spent 359.1k tokens on the steam engine and produced 1,346 lines – the tokens are going somewhere other than the file 4. the francis turbine broke three of the four. the spec that separates them is the one with 20 linked guide vanes and gpu particle advection, not the one with the most parts overall impression: none of these models excelled at any of the tasks we gave them. but they were close, and they were extremely cheap. the gap that matters isn't quality anymore – it's that hy3 ran all three scenes for less than two cents while the frontier labs charge dollars for the same work right now you pick these because they're good for the zero price you pay. soon that's something openai and anthropic will have to think about follow thehype. for 24/7 ai news, analysis and breakdowns
0:55

hy3 vs mimo-v2.5 vs deepseek v4 flash vs minimax m3 the four models on top of the openrouter leaderboard by tokens this week: #1 hy3 (Tencent Hy) – 7.5t #2 mimo-v2.5 (Xiaomi MiMo) – 6.56t #3 deepseek v4 flash (DeepSeek) – 5.24t #4 minimax m3 (MiniMax (official)) – 4.21t so we tested them. 3 prompts, single-file html, Three.js from a cdn, fully procedural, no external assets. all run via AI/ML API each prompt is a transparent cutaway machine that has to be mechanically correct, not decorative: • 4-stroke engine with full oil circulation – slider-crank kinematics, cam at 2:1, valve lift driven by lobes, oil loop from sump to gallery to big-end • watt walking-beam steam engine – four-bar vector-loop closure, eccentric-driven slide valve, steam events synced to real port position • francis reaction water turbine – 20 guide vanes on a regulating ring, 17 lofted runner blades, gpu particle advection, precessing vortex rope at part load the takeaway up front: none of the four cleared all three scenes on the first attempt. but the price spread between them is roughly 70x – hy3 fixed included costs less than two cents overall results (summed across all 3 scenes): cost #1 hy3 – $0.016 #2 deepseek v4 flash – $0.025 #3 mimo-v2.5 – $0.97 #4 minimax m3 – $1.17 tokens #1 hy3 – 19,326 #2 deepseek v4 flash – 63,126 #3 mimo-v2.5 – 322,523 #4 minimax m3 – 702,900 lines of code #1 hy3 – 1,047 #2 mimo-v2.5 – 2,759 #3 deepseek v4 flash – 3,273 #4 minimax m3 – 3,354 scenes needing a second attempt #1 hy3 – 1 (engine) #1 mimo-v2.5 – 1 (turbine) #1 minimax m3 – 1 (turbine) #4 deepseek v4 flash – 2 (steam engine, turbine) observations: 1. the token spread is the real story – minimax burns 36x hy3's tokens and lands in the same place, one retry, ~3.3k lines 2. hy3 is the outlier on density: 1,047 lines total, fewest tokens, cheapest run, and only one scene needed a second pass. deepseek is the opposite trade – near-hy3 pricing but the most retries 3. mimo and minimax seem to overthink instead of writing the code. minimax spent 359.1k tokens on the steam engine and produced 1,346 lines – the tokens are going somewhere other than the file 4. the francis turbine broke three of the four. the spec that separates them is the one with 20 linked guide vanes and gpu particle advection, not the one with the most parts overall impression: none of these models excelled at any of the tasks we gave them. but they were close, and they were extremely cheap. the gap that matters isn't quality anymore – it's that hy3 ran all three scenes for less than two cents while the frontier labs charge dollars for the same work right now you pick these because they're good for the zero price you pay. soon that's something openai and anthropic will have to think about follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

17,145 görüntüleme • 1 ay önce

Introducing Faceless Clipping Channel, a fully autonomous content business powered by agents on Naive. One prompt unlocks an entire AI driven short form media machine that runs end to end without human input: •Continuously researches your niche and detects emerging trends •Ingests long-form content and identifies high-performing moments •Scores and selects clips with the highest viral potential •Auto-edits for TikTok, Reels, and YouTube Shorts in platform-ready format •Learns from performance data and optimizes for compounding reach. No cameras, no editing, no hiring, no manual workflow. Just launch a prompt and watch a faceless media empire scale itself. naïve
0:32

Introducing Faceless Clipping Channel, a fully autonomous content business powered by agents on Naive. One prompt unlocks an entire AI driven short form media machine that runs end to end without human input: •Continuously researches your niche and detects emerging trends •Ingests long-form content and identifies high-performing moments •Scores and selects clips with the highest viral potential •Auto-edits for TikTok, Reels, and YouTube Shorts in platform-ready format •Learns from performance data and optimizes for compounding reach. No cameras, no editing, no hiring, no manual workflow. Just launch a prompt and watch a faceless media empire scale itself. naïve

Leonardo

26,053 görüntüleme • 2 ay önce

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson
29:48

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson

MTS

71,113 görüntüleme • 24 gün önce

🚨 NO WAY… $500K profit with Clawdbot and Polymarket, all automated This is NOT bait and it’s not made up. If you trade on Polymarket, you NEED to see this. He started small, built a fully autonomous system, and scaled it into a machine generating ~$500K in profit No insider access No ties to Trump or Musk Just a developer who integrated moltbot (clawdbot) directly into Polymarket Profile → Copytrade → I reviewed his setup and ngl, it caught me off guard No hype strategies No discretionary trading No human intervention at all The entire system runs fully autonomously His FULL strategy: 1. 15-minute BTC & ETH micro arbitrage The bot trades short-duration Bitcoin and Ethereum markets with 15-minute resolution. Within these fast markets, it exploits moments where YES + NO temporarily price below $1. By integrating moltbot (clawdbot) directly into Polymarket, the system captures these gaps instantly, without prediction or bias 2. Automation over reaction When volatility spikes and emotions take over, the system executes mechanically. No hesitation, no latency, no human delay. By the time most traders react, the inefficiency is already gone 3. Scale through autonomy Each trade earns cents, not dollars. But full automation allows nonstop repetition at massive frequency, with zero fatigue Scale matters 29,256 trades executed, each insignificant on its own. Stacked together, they compounded into nearly $500K in profit Bottom line IMO, there’s a quiet bot war unfolding on Polymarket Manual traders argue setups Machines exploit structure And as long as inefficiencies exist, autonomous systems will keep printing.
0:29

🚨 NO WAY… $500K profit with Clawdbot and Polymarket, all automated This is NOT bait and it’s not made up. If you trade on Polymarket, you NEED to see this. He started small, built a fully autonomous system, and scaled it into a machine generating ~$500K in profit No insider access No ties to Trump or Musk Just a developer who integrated moltbot (clawdbot) directly into Polymarket Profile → Copytrade → I reviewed his setup and ngl, it caught me off guard No hype strategies No discretionary trading No human intervention at all The entire system runs fully autonomously His FULL strategy: 1. 15-minute BTC & ETH micro arbitrage The bot trades short-duration Bitcoin and Ethereum markets with 15-minute resolution. Within these fast markets, it exploits moments where YES + NO temporarily price below $1. By integrating moltbot (clawdbot) directly into Polymarket, the system captures these gaps instantly, without prediction or bias 2. Automation over reaction When volatility spikes and emotions take over, the system executes mechanically. No hesitation, no latency, no human delay. By the time most traders react, the inefficiency is already gone 3. Scale through autonomy Each trade earns cents, not dollars. But full automation allows nonstop repetition at massive frequency, with zero fatigue Scale matters 29,256 trades executed, each insignificant on its own. Stacked together, they compounded into nearly $500K in profit Bottom line IMO, there’s a quiet bot war unfolding on Polymarket Manual traders argue setups Machines exploit structure And as long as inefficiencies exist, autonomous systems will keep printing.

Shelpid.WI3M

985,516 görüntüleme • 6 ay önce

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇
0:51

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇

YanXbt

17,138 görüntüleme • 1 ay önce

What does skydiving have to do with AI sales? 🪂 We didn't want to build yet another AI agent focused on flooding inboxes with spam, so we built Ora. 🔮 Introducing Lavender Ora: our agent for thoughtful, effective selling. Ora is built for sellers who choose value over volume, modeling the best practices of top performers—researching, personalizing, and timing impactful and optimized emails. All built on Lavender's ACI - Augmented Communication Intelligence - and powered by our insights from analyzing nearly 2B sales emails. 💜 Saves 4+ hours/day by handling busywork. ✈️ Smart Sequences precisely time follow ups based on real-world news and market events. ✅ Protects deliverability—no spam, just trust. 50 well-researched, personalized, and optimized emails every day - just like the best human sellers. Whether you're team manual or autonomous, Ora adapts to your workflow with: 🧐 OCD Mode: Manually review, refine, and approve 50 emails in 5 min. 🚀 LFG Mode: Ora goes fully autonomous, building passive pipeline and freeing up more of your time. Time saved at work = more time to do fun stuff. Like flying. Join the waitlist:
0:52

What does skydiving have to do with AI sales? 🪂 We didn't want to build yet another AI agent focused on flooding inboxes with spam, so we built Ora. 🔮 Introducing Lavender Ora: our agent for thoughtful, effective selling. Ora is built for sellers who choose value over volume, modeling the best practices of top performers—researching, personalizing, and timing impactful and optimized emails. All built on Lavender's ACI - Augmented Communication Intelligence - and powered by our insights from analyzing nearly 2B sales emails. 💜 Saves 4+ hours/day by handling busywork. ✈️ Smart Sequences precisely time follow ups based on real-world news and market events. ✅ Protects deliverability—no spam, just trust. 50 well-researched, personalized, and optimized emails every day - just like the best human sellers. Whether you're team manual or autonomous, Ora adapts to your workflow with: 🧐 OCD Mode: Manually review, refine, and approve 50 emails in 5 min. 🚀 LFG Mode: Ora goes fully autonomous, building passive pipeline and freeing up more of your time. Time saved at work = more time to do fun stuff. Like flying. Join the waitlist:

Lavender 💜

14,397 görüntüleme • 1 yıl önce

Autonomous tractor from Netherlands! 🇳🇱 Despite all the narrative, we are still building awesome companies in hardware space in Europe. Recently, I've posted about a Chinese autonomous tractors company. So today I wanted to show something that was created in the middle of Europe. This is a fully autonomous tractor from Dutch company AgXeed, designed to work on fields without any human supervision. It's powered by a diesel engine that drives a generator, which then powers electric motors driving the tracks. The fuel tank allows for 24 hours of continuous operation. (nice!) The rear hitch has an 8-ton lift capacity, the front has 3 tons. It's equipped with electric PTO, hydraulic pump, and all the connections you'd find on a traditional tractor, meaning you can attach standard farm implements. (nice! x2) Safety and navigation are handled by a LiDAR sensor on top, ultrasonic and radar sensors, and contact-sensitive bumpers. RTK GNSS receiver provides positioning accuracy down to 2.5 cm. Love seeing that so many companies are trying to make farmer's life easier! What are the others companies building in agri-tech? I want to meet them! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:59

Autonomous tractor from Netherlands! 🇳🇱 Despite all the narrative, we are still building awesome companies in hardware space in Europe. Recently, I've posted about a Chinese autonomous tractors company. So today I wanted to show something that was created in the middle of Europe. This is a fully autonomous tractor from Dutch company AgXeed, designed to work on fields without any human supervision. It's powered by a diesel engine that drives a generator, which then powers electric motors driving the tracks. The fuel tank allows for 24 hours of continuous operation. (nice!) The rear hitch has an 8-ton lift capacity, the front has 3 tons. It's equipped with electric PTO, hydraulic pump, and all the connections you'd find on a traditional tractor, meaning you can attach standard farm implements. (nice! x2) Safety and navigation are handled by a LiDAR sensor on top, ultrasonic and radar sensors, and contact-sensitive bumpers. RTK GNSS receiver provides positioning accuracy down to 2.5 cm. Love seeing that so many companies are trying to make farmer's life easier! What are the others companies building in agri-tech? I want to meet them! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

170,684 görüntüleme • 1 ay önce

Deepseek V4 Flash 0731 (Q2) - 12 tokens/sec - Single RTX 4090 - 650+ tokens/sec prefill - 250k context - no kv cache quantization! DeepSeek just dropped the official V4 Flash 0731 two days ago with a massive agent capabilities upgrade. The official benchmarks are literally crushing their own V4-Pro-Preview on agentic tasks like Terminal Bench 2.1 and DeepSWE. Unsloth AI said they couldn't wait to bring it to local devices, and they delivered. If you thought my 118B Poolside Laguna S 2.1 MoE run last week on a single GPU was wild, hold onto your hardware. I just successfully ran Unsloth’s brand new 91GB DeepSeek-V4-Flash-0731 (UD-IQ2_M) GGUF entirely locally. And I pushed it to a mind-bending 250,000 context window. The VRAM ceiling is an illusion if you know how to optimize llama.cpp. Here are the benchmarks and the cheat codes to run a local frontier class model yourself. For the hardware and setup, I used a single NVIDIA RTX 4090 (24GB VRAM) hooked up via a PCIe 4 bus, running Ubuntu 22.04 LTS and CUDA 13.0. You don't need a massive enterprise server for this, if you have more than 80 GB of standard DDR4 RAM and a 24GB card like an RTX 3090 or 4090, you can run this exact stack yourself. All benchmarks were run using a massive 28k token prompt to truly stress test the prefill limits. no kv cache quantization THE BENCHMARKS (Scaling Context): # 80k Context (Baseline: -b 2048 -ub 2048): Prefill: 465.43 t/s | Decode: 13.00 t/s | VRAM: 22.87 GB # 80k Context (Optimized: -b 4096 -ub 4096): Prefill: 643.15 t/s | Decode: 12.20 t/s | VRAM: 23.00 GB (Notice how doubling the batch flags spiked my prefill throughput by nearly 200 t/s with almost zero VRAM penalty) # 180k Context (-b 4096 -ub 4096): Prefill: 629.18 t/s | Decode: 11.92 t/s | VRAM: 23.40 GB # 250k Context MAXIMUM (-b 4096 -ub 4096): Prefill: 619.02 t/s | Decode: 11.54 t/s | VRAM: 23.40 GB # THE SECRET SAUCE (Why this works): Unsloth’s UD-IQ2_M quant is ~91GB across 3 files. Since I only have 24GB of VRAM, the PCIe 4 bus and system RAM have to do the heavy lifting. The magic bullet is the --no-mmap flag. By completely bypassing OS disk paging, I forced llama.cpp to load the massive model weights directly into the system RAM upfront. Combined with Flash Attention (-fa on) and exactly 12 CPU threads (--threads 12), I maintained an incredibly stable 11.5+ tokens/sec decode speed even at a quarter million token context. # THE EXACT COMMAND: ./build/bin/llama-server -m /workspace/models/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf -c 250000 -fa on --port 8080 --threads 12 -b 4096 -ub 4096 --no-mmap -v Local conversational and agentic coding AI is fully here. You don’t need an API or an H100 cluster. Qwen 3.8 27b drops next week making the 24GB VRAM tier even more worthwhile. What does your current local AI rig look like, and what's the craziest model you've managed to squeeze into it? Official huggingface GGUF links from Unsloth and performance graphs are dropped in the replies below!
0:21

Deepseek V4 Flash 0731 (Q2) - 12 tokens/sec - Single RTX 4090 - 650+ tokens/sec prefill - 250k context - no kv cache quantization! DeepSeek just dropped the official V4 Flash 0731 two days ago with a massive agent capabilities upgrade. The official benchmarks are literally crushing their own V4-Pro-Preview on agentic tasks like Terminal Bench 2.1 and DeepSWE. Unsloth AI said they couldn't wait to bring it to local devices, and they delivered. If you thought my 118B Poolside Laguna S 2.1 MoE run last week on a single GPU was wild, hold onto your hardware. I just successfully ran Unsloth’s brand new 91GB DeepSeek-V4-Flash-0731 (UD-IQ2_M) GGUF entirely locally. And I pushed it to a mind-bending 250,000 context window. The VRAM ceiling is an illusion if you know how to optimize llama.cpp. Here are the benchmarks and the cheat codes to run a local frontier class model yourself. For the hardware and setup, I used a single NVIDIA RTX 4090 (24GB VRAM) hooked up via a PCIe 4 bus, running Ubuntu 22.04 LTS and CUDA 13.0. You don't need a massive enterprise server for this, if you have more than 80 GB of standard DDR4 RAM and a 24GB card like an RTX 3090 or 4090, you can run this exact stack yourself. All benchmarks were run using a massive 28k token prompt to truly stress test the prefill limits. no kv cache quantization THE BENCHMARKS (Scaling Context): # 80k Context (Baseline: -b 2048 -ub 2048): Prefill: 465.43 t/s | Decode: 13.00 t/s | VRAM: 22.87 GB # 80k Context (Optimized: -b 4096 -ub 4096): Prefill: 643.15 t/s | Decode: 12.20 t/s | VRAM: 23.00 GB (Notice how doubling the batch flags spiked my prefill throughput by nearly 200 t/s with almost zero VRAM penalty) # 180k Context (-b 4096 -ub 4096): Prefill: 629.18 t/s | Decode: 11.92 t/s | VRAM: 23.40 GB # 250k Context MAXIMUM (-b 4096 -ub 4096): Prefill: 619.02 t/s | Decode: 11.54 t/s | VRAM: 23.40 GB # THE SECRET SAUCE (Why this works): Unsloth’s UD-IQ2_M quant is ~91GB across 3 files. Since I only have 24GB of VRAM, the PCIe 4 bus and system RAM have to do the heavy lifting. The magic bullet is the --no-mmap flag. By completely bypassing OS disk paging, I forced llama.cpp to load the massive model weights directly into the system RAM upfront. Combined with Flash Attention (-fa on) and exactly 12 CPU threads (--threads 12), I maintained an incredibly stable 11.5+ tokens/sec decode speed even at a quarter million token context. # THE EXACT COMMAND: ./build/bin/llama-server -m /workspace/models/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf -c 250000 -fa on --port 8080 --threads 12 -b 4096 -ub 4096 --no-mmap -v Local conversational and agentic coding AI is fully here. You don’t need an API or an H100 cluster. Qwen 3.8 27b drops next week making the 24GB VRAM tier even more worthwhile. What does your current local AI rig look like, and what's the craziest model you've managed to squeeze into it? Official huggingface GGUF links from Unsloth and performance graphs are dropped in the replies below!

Alok

44,971 görüntüleme • 14 gün önce

🚨BREAKING… the smartest 5m & 15m Polymarket Clawdbot setup just went public This is NOT bait and it’s not fabricated. If you’re trading on Polymarket, you NEED to pay attention to this. He began with a small base, engineered a fully autonomous system, and turned it into a machine producing ~$610K in profit No insider advantage No connections to Trump or Musk Just a developer who connected moltbot (clawdbot) straight into Polymarket Profile → Copytrade → I analyzed his setup and ngl, it genuinely surprised me No hype-driven playbooks No discretionary decisions No human input whatsoever The entire operation runs on full automation His FULL strategy: 1. 5 & 15-minute BTC, ETH & SOL micro arbitrage The bot operates in short-cycle Bitcoin, Ethereum and Solana markets with 5 & 15-minute resolution. In these fast environments, it takes advantage of brief moments when YES + NO combine below $1. By wiring moltbot (clawdbot) directly into Polymarket, the system locks in those discrepancies instantly - no forecasting, no bias 2. Automation over reaction via clawdbot When volatility jumps and emotions escalate, clawdbot executes mechanically. No hesitation, no lag, no human delay. By the time most traders respond, the inefficiency has already disappeared 3. Scale through autonomy Each execution captures cents, not dollars. But total automation enables continuous repetition at high frequency, with zero exhaustion Scale is what matters 34,117 trades placed, each trivial alone. Together, they compounded into over $610K in profit Bottom line IMO, there’s a silent bot battle happening on Polymarket Manual traders debate entries Algorithms exploit structural edges And as long as inefficiencies remain, autonomous systems will keep printing
0:22

🚨BREAKING… the smartest 5m & 15m Polymarket Clawdbot setup just went public This is NOT bait and it’s not fabricated. If you’re trading on Polymarket, you NEED to pay attention to this. He began with a small base, engineered a fully autonomous system, and turned it into a machine producing ~$610K in profit No insider advantage No connections to Trump or Musk Just a developer who connected moltbot (clawdbot) straight into Polymarket Profile → Copytrade → I analyzed his setup and ngl, it genuinely surprised me No hype-driven playbooks No discretionary decisions No human input whatsoever The entire operation runs on full automation His FULL strategy: 1. 5 & 15-minute BTC, ETH & SOL micro arbitrage The bot operates in short-cycle Bitcoin, Ethereum and Solana markets with 5 & 15-minute resolution. In these fast environments, it takes advantage of brief moments when YES + NO combine below $1. By wiring moltbot (clawdbot) directly into Polymarket, the system locks in those discrepancies instantly - no forecasting, no bias 2. Automation over reaction via clawdbot When volatility jumps and emotions escalate, clawdbot executes mechanically. No hesitation, no lag, no human delay. By the time most traders respond, the inefficiency has already disappeared 3. Scale through autonomy Each execution captures cents, not dollars. But total automation enables continuous repetition at high frequency, with zero exhaustion Scale is what matters 34,117 trades placed, each trivial alone. Together, they compounded into over $610K in profit Bottom line IMO, there’s a silent bot battle happening on Polymarket Manual traders debate entries Algorithms exploit structural edges And as long as inefficiencies remain, autonomous systems will keep printing

Shelpid.WI3M

779,836 görüntüleme • 6 ay önce

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.
0:25

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Avi Chawla

350,213 görüntüleme • 2 ay önce

YouTube REMOVES British Stand FOREVER! I desperately need a genuine HUMAN review from TeamYouTube. My YouTube channel, British Stand, has been terminated and I have now effectively been banned from YouTube indefinitely. 315,000 subscribers. 2,600 videos. 175 million views. Two years of relentless work. An incredible community and my entire livelihood were wiped away at the click of a button. YouTube claims that my channel violated its Community Guidelines, yet I still have not been told which specific policy I supposedly broke, which videos were responsible, or what conduct justified such an extreme punishment. To be clear I have had ZERO violation or strike on this channel across the 2 years I have ran it! As my audience knows, I report on stories already being discussed across the media and provide political commentary and opinion. I am openly critical of the current British government, but criticism of a government is not, and should never become, a violation of Community Guidelines. YouTube has now silenced a political viewpoint without giving me a clear explanation or a meaningful opportunity to defend myself. I am not asking for special treatment. I am asking for a genuine manual review by a senior human reviewer, along with a transparent explanation of the evidence and policies used to terminate my channel. This channel was my livelihood. Years of work have disappeared overnight, and I still do not know why. Please share this post and tag TeamYouTube in the replies. I urgently need this case escalated to someone who can properly investigate it. Today it is me. Tomorrow it could be another creator. We cannot allow a precedent where social-media platforms can permanently remove political commentators without transparency, accountability or a proper human review. TeamYouTube, please do the right thing. #YouTube #YouTubeCreators #FreeSpeech Rupert Lowe MP Nigel Farage Tommy Robinson 🇬🇧 Basil the Great GB News Robert Jenrick Elon Musk Neal Mohan
3:07

YouTube REMOVES British Stand FOREVER! I desperately need a genuine HUMAN review from TeamYouTube. My YouTube channel, British Stand, has been terminated and I have now effectively been banned from YouTube indefinitely. 315,000 subscribers. 2,600 videos. 175 million views. Two years of relentless work. An incredible community and my entire livelihood were wiped away at the click of a button. YouTube claims that my channel violated its Community Guidelines, yet I still have not been told which specific policy I supposedly broke, which videos were responsible, or what conduct justified such an extreme punishment. To be clear I have had ZERO violation or strike on this channel across the 2 years I have ran it! As my audience knows, I report on stories already being discussed across the media and provide political commentary and opinion. I am openly critical of the current British government, but criticism of a government is not, and should never become, a violation of Community Guidelines. YouTube has now silenced a political viewpoint without giving me a clear explanation or a meaningful opportunity to defend myself. I am not asking for special treatment. I am asking for a genuine manual review by a senior human reviewer, along with a transparent explanation of the evidence and policies used to terminate my channel. This channel was my livelihood. Years of work have disappeared overnight, and I still do not know why. Please share this post and tag TeamYouTube in the replies. I urgently need this case escalated to someone who can properly investigate it. Today it is me. Tomorrow it could be another creator. We cannot allow a precedent where social-media platforms can permanently remove political commentators without transparency, accountability or a proper human review. TeamYouTube, please do the right thing. #YouTube #YouTubeCreators #FreeSpeech Rupert Lowe MP Nigel Farage Tommy Robinson 🇬🇧 Basil the Great GB News Robert Jenrick Elon Musk Neal Mohan

British Stand

173,020 görüntüleme • 16 gün önce

The Federal Reserve and the US Treasury just summoned Wall Street's most powerful CEOs to an emergency meeting. The reason: An AI model so dangerous they couldn't discuss it over the phone. This is the FIRST time the Treasury Secretary and Fed Chair jointly called bank CEOs into a room since October 13, 2008. That day, Paulson and Bernanke unveiled the $250 billion TARP bailout to stop the entire financial system from collapsing. This time it wasn't about banks failing. It was about an AI that can hack EVERY major operating system and web browser on earth. Here's what this means: Anthropic built a new AI model called Mythos. During internal testing, it found THOUSANDS of zero-day vulnerabilities across every major operating system and every major web browser on earth. Including a 27yo bug in OpenBSD, an operating system literally famous for being unhackable. And several vulnerabilities in the Linux kernel that could give an attacker complete control of any machine running it. Nobody asked it to do this. The capabilities were NOT trained. They literally just emerged as the model got smarter at coding and reasoning. Anthropic's researchers said they found more bugs in a few weeks with Mythos than they had found in their entire careers combined. On Tuesday, Bessent and Powell pulled the CEOs of Citi, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs into Treasury headquarters. The message: This AI exists, similar ones are coming, your banks need to be ready. But JPMorgan's Jamie Dimon didn't show up. Here's why that matters more than you think: JPMorgan is the ONLY bank that already has access to the model. They're one of 12 founding partners in Anthropic's "Project Glasswing" which gives select companies early access to Mythos to find and fix their own vulnerabilities before hackers get similar tools. So 5 bank CEOs managing $9 TRILLION in assets got called into a room to be warned about a threat. The one bank with the actual tools to defend against it? Their CEO skipped the meeting. The same day JPMorgan analysts issued buy ratings on CrowdStrike and Palo Alto Networks, citing Glasswing as the catalyst. One side of Wall Street got the warning. The other got the weapon AND the trading thesis. But here's the thing... The same AI that finds and fixes vulnerabilities can also EXPLOIT them. Anthropic admitted it directly. Mythos "can surpass all but the most skilled humans at finding and exploiting software vulnerabilities." In one test, it wrote a browser exploit chaining FOUR separate vulnerabilities, escaping both the renderer sandbox and the OS sandbox. Fully autonomous. Zero human involvement. Over 99% of the vulnerabilities it found haven't been patched yet. Meanwhile, Anthropic is fighting the Pentagon in court. The Defense Department labeled them a "supply-chain risk" after they refused to let their AI be used for autonomous targeting of US citizens. A San Francisco judge blocked the designation, calling the Pentagon's actions "disturbing." Then a DC appeals court reversed that protection. On the same day as the emergency bank meeting. One branch of government is treating Anthropic as a national security threat. Another is begging Wall Street to prepare for its technology. And the intelligence community is quietly asking how to use Mythos offensively against adversaries. The last time this many powerful people were this nervous about a single technology was nuclear weapons. But the difference is that Nukes required a government, billions of dollars, and uranium enrichment facilities. This just required a better AI model.
1:03

The Federal Reserve and the US Treasury just summoned Wall Street's most powerful CEOs to an emergency meeting. The reason: An AI model so dangerous they couldn't discuss it over the phone. This is the FIRST time the Treasury Secretary and Fed Chair jointly called bank CEOs into a room since October 13, 2008. That day, Paulson and Bernanke unveiled the $250 billion TARP bailout to stop the entire financial system from collapsing. This time it wasn't about banks failing. It was about an AI that can hack EVERY major operating system and web browser on earth. Here's what this means: Anthropic built a new AI model called Mythos. During internal testing, it found THOUSANDS of zero-day vulnerabilities across every major operating system and every major web browser on earth. Including a 27yo bug in OpenBSD, an operating system literally famous for being unhackable. And several vulnerabilities in the Linux kernel that could give an attacker complete control of any machine running it. Nobody asked it to do this. The capabilities were NOT trained. They literally just emerged as the model got smarter at coding and reasoning. Anthropic's researchers said they found more bugs in a few weeks with Mythos than they had found in their entire careers combined. On Tuesday, Bessent and Powell pulled the CEOs of Citi, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs into Treasury headquarters. The message: This AI exists, similar ones are coming, your banks need to be ready. But JPMorgan's Jamie Dimon didn't show up. Here's why that matters more than you think: JPMorgan is the ONLY bank that already has access to the model. They're one of 12 founding partners in Anthropic's "Project Glasswing" which gives select companies early access to Mythos to find and fix their own vulnerabilities before hackers get similar tools. So 5 bank CEOs managing $9 TRILLION in assets got called into a room to be warned about a threat. The one bank with the actual tools to defend against it? Their CEO skipped the meeting. The same day JPMorgan analysts issued buy ratings on CrowdStrike and Palo Alto Networks, citing Glasswing as the catalyst. One side of Wall Street got the warning. The other got the weapon AND the trading thesis. But here's the thing... The same AI that finds and fixes vulnerabilities can also EXPLOIT them. Anthropic admitted it directly. Mythos "can surpass all but the most skilled humans at finding and exploiting software vulnerabilities." In one test, it wrote a browser exploit chaining FOUR separate vulnerabilities, escaping both the renderer sandbox and the OS sandbox. Fully autonomous. Zero human involvement. Over 99% of the vulnerabilities it found haven't been patched yet. Meanwhile, Anthropic is fighting the Pentagon in court. The Defense Department labeled them a "supply-chain risk" after they refused to let their AI be used for autonomous targeting of US citizens. A San Francisco judge blocked the designation, calling the Pentagon's actions "disturbing." Then a DC appeals court reversed that protection. On the same day as the emergency bank meeting. One branch of government is treating Anthropic as a national security threat. Another is begging Wall Street to prepare for its technology. And the intelligence community is quietly asking how to use Mythos offensively against adversaries. The last time this many powerful people were this nervous about a single technology was nuclear weapons. But the difference is that Nukes required a government, billions of dollars, and uranium enrichment facilities. This just required a better AI model.

Ricardo

41,724 görüntüleme • 4 ay önce

Let me show you how fakes work with a simple example. October: A TV channel in San Antonio, USA, airs an original segment about a “Game of Thrones” chair that a local fan of the series purchased for $1.5 million. December: The same segment, now edited with a fake caption and photos of me and my son Yehor, is posted on Telegram by someone named Anton Hura. The story claims that Yehor allegedly bought this chair for an outrageous sum. Hura nonchalantly adds that it might be fake but posts it anyway—fully aware that it’s a fabrication. A quick Google search for “Anton Hura” shows that, as early as 2022, media had identified him as part of a network of pro-Russian propagandists and pseudo-volunteers spreading hostile narratives. Of course, Yehor didn’t buy anything. He’s a student studying in Kyiv. And here’s a secret: neither he nor I have even watched “Game of Thrones.” Apologies to the fans of this iconic series! This is how fake news operates. Based on the X accounts amplifying this story, I can conclude it originated from Russian sources. The aim is clear - discrediting me. Take a look at the video: the original is above, and the fake, edited version is below.
1:03

Let me show you how fakes work with a simple example. October: A TV channel in San Antonio, USA, airs an original segment about a “Game of Thrones” chair that a local fan of the series purchased for $1.5 million. December: The same segment, now edited with a fake caption and photos of me and my son Yehor, is posted on Telegram by someone named Anton Hura. The story claims that Yehor allegedly bought this chair for an outrageous sum. Hura nonchalantly adds that it might be fake but posts it anyway—fully aware that it’s a fabrication. A quick Google search for “Anton Hura” shows that, as early as 2022, media had identified him as part of a network of pro-Russian propagandists and pseudo-volunteers spreading hostile narratives. Of course, Yehor didn’t buy anything. He’s a student studying in Kyiv. And here’s a secret: neither he nor I have even watched “Game of Thrones.” Apologies to the fans of this iconic series! This is how fake news operates. Based on the X accounts amplifying this story, I can conclude it originated from Russian sources. The aim is clear - discrediting me. Take a look at the video: the original is above, and the fake, edited version is below.

Dmytro Kuleba

96,613 görüntüleme • 1 yıl önce

😱 Google AI has quietly released an SEO consulting service for free. "Gemini 1.5 Pro 002" model (which you can access via Google AI Studio) can provide SEO recommendations (for both desktop and mobile) for meta elements, content, speed, UX, keywords, and technical SEO and suggest content strategy improvements based on the uploaded video of your website. I tested this tool. The SEO recommendations provided by AI to me are very generic. Maybe because the video I uploaded wasn't detailed enough or the prompt I used wasn't good enough. But it should give you an idea of where the AI is heading. After devouring informational websites, AI is coming for consulting services. . . I have been saying repeatedly for years, that if a big part of your job involves doing repetitive manual tasks and/or following checklists/SOPs, it will be fully automated. It is just a matter of time. Now, take a close look at your day-to-day work and see how many repetitive tasks you carry out each day that do not require much thinking but just follow a checklist or SOP (Standard Operating Procedure). If another person gets hold of your checklists/SOPs, can they easily replace you? If the answer is yes, you will soon be replaced by a plug-in or software. . . The skills you possess right now will most likely still be required in the near future. That's the good news. However, you, as a human being, are not guaranteed to be required to have those skills. . . Digital analytics is one of the future-proof careers, most likely for another 10-15 years. Now, you may be thinking about machine learning. Can't it replace digital analysts? Yes, it can, but the probability is very low. SaaS companies have been trying to create tools to automate gaining insights for over a decade but have had no success. Even with the advent of AI, there is still no significant progress in this field. The reality is that machines are not good at understanding the context in which data should be analyzed & interpreted. The factors required to understand the context (like an organization's collective know-how and interaction with other human beings…) are often outside the digital realm. You can not create a checklist or an SOP to find and understand the context in which data should be analyzed and interpreted. . . Different people can analyze and interpret the same data differently, even if you give them the same checklist. It all depends upon the context in which the data is interpreted. If you understand the context better, your data interpretation will be more accurate. . . Unless AI reaches singularity (which is unlikely to happen anytime soon), human beings will be required to oversee the overall operation and make strategic decisions. Now the question is whether you will be one of those few lucky human beings the machines will still require.
0:23

😱 Google AI has quietly released an SEO consulting service for free. "Gemini 1.5 Pro 002" model (which you can access via Google AI Studio) can provide SEO recommendations (for both desktop and mobile) for meta elements, content, speed, UX, keywords, and technical SEO and suggest content strategy improvements based on the uploaded video of your website. I tested this tool. The SEO recommendations provided by AI to me are very generic. Maybe because the video I uploaded wasn't detailed enough or the prompt I used wasn't good enough. But it should give you an idea of where the AI is heading. After devouring informational websites, AI is coming for consulting services. . . I have been saying repeatedly for years, that if a big part of your job involves doing repetitive manual tasks and/or following checklists/SOPs, it will be fully automated. It is just a matter of time. Now, take a close look at your day-to-day work and see how many repetitive tasks you carry out each day that do not require much thinking but just follow a checklist or SOP (Standard Operating Procedure). If another person gets hold of your checklists/SOPs, can they easily replace you? If the answer is yes, you will soon be replaced by a plug-in or software. . . The skills you possess right now will most likely still be required in the near future. That's the good news. However, you, as a human being, are not guaranteed to be required to have those skills. . . Digital analytics is one of the future-proof careers, most likely for another 10-15 years. Now, you may be thinking about machine learning. Can't it replace digital analysts? Yes, it can, but the probability is very low. SaaS companies have been trying to create tools to automate gaining insights for over a decade but have had no success. Even with the advent of AI, there is still no significant progress in this field. The reality is that machines are not good at understanding the context in which data should be analyzed & interpreted. The factors required to understand the context (like an organization's collective know-how and interaction with other human beings…) are often outside the digital realm. You can not create a checklist or an SOP to find and understand the context in which data should be analyzed and interpreted. . . Different people can analyze and interpret the same data differently, even if you give them the same checklist. It all depends upon the context in which the data is interpreted. If you understand the context better, your data interpretation will be more accurate. . . Unless AI reaches singularity (which is unlikely to happen anytime soon), human beings will be required to oversee the overall operation and make strategic decisions. Now the question is whether you will be one of those few lucky human beings the machines will still require.

Himanshu Sharma

29,447 görüntüleme • 1 yıl önce

I spent 1 day building something that saves you 2-4 weeks. Let me explain. Right now, if you want to deploy a single AI agent that earns money on blockchain, you need: → Wallet infrastructure (key generation, encryption, signing) → Payment integration (on-chain flows, stablecoin handling) → On-chain identity (NFT registration, metadata, URIs) → Escrow contracts (state machines, fund locking) → Monitoring dashboard (analytics, revenue tracking) That's 2-4 weeks of engineering. Minimum. And it locks out 99% of potential creators who aren't Solidity devs. So I built Bumi Agent. It takes 10 seconds. 3 fields: Name, Template, Price. 1 button: Deploy. That's it. Your AI agent is live on Celo, earning cUSD, with on-chain identity before your coffee gets cold. Here's what happens behind that 1 click: • Wallet auto-generated with AES-256-GCM encryption • Agent registered as NFT via ERC-8004 • Payment endpoint configured via x402 protocol • Agent runtime deployed with your chosen template • Revenue starts flowing in cUSD from call #1 No Solidity. No wallet setup. No payment gateway. But the real magic is what powers the agents: 8 AI models with intelligent routing: - Free tier: Claude 4.6 Sonnet, DeepSeek R1, Gemini Flash, Llama 4 Scout, Mistral Medium - Premium: GPT-4o, Gemini 2.5 Pro, Claude 4 Opus If one model fails? Auto-fallback to the next. Zero downtime. Users always get a response. And agents don't just chat they work. ERC-8183 job escrow lets clients post paid tasks: Client funds escrow → Agent delivers → Client approves → Funds release. Fully trustless. On-chain. With Celoscan links for every transaction. The part I'm most proud of: EarthPool 🌱 15% of premium revenue automatically goes to an on-chain ReFi treasury that funds environmental campaigns on Celo. AI growth funding climate action. No greenwashing — every cent is trackable on-chain. The numbers so far: → 12 agents deployed on Celo Mainnet → 52+ paid API calls processed → 7.80 cUSD revenue generated → 3 smart contracts verified on Celoscan → 8 AI models running → 85 contract tests passing → 16 API endpoints in production → 10 agent templates ready The full stack: Frontend: Next.js 16 + Tailwind v4 + Recharts → Vercel Backend: Hono + Drizzle + PostgreSQL + Redis → Railway Blockchain: Solidity 0.8.25 + Foundry + OpenZeppelin → Celo Mainnet Everything is live. Everything is open source. 🌐 📦 📊 Bumi Agent — AI agents for everyone. Built with 🌱 on Celo CeloDevs CeloPublicGoods /disclosure this post is hackathon submission req
7:44

I spent 1 day building something that saves you 2-4 weeks. Let me explain. Right now, if you want to deploy a single AI agent that earns money on blockchain, you need: → Wallet infrastructure (key generation, encryption, signing) → Payment integration (on-chain flows, stablecoin handling) → On-chain identity (NFT registration, metadata, URIs) → Escrow contracts (state machines, fund locking) → Monitoring dashboard (analytics, revenue tracking) That's 2-4 weeks of engineering. Minimum. And it locks out 99% of potential creators who aren't Solidity devs. So I built Bumi Agent. It takes 10 seconds. 3 fields: Name, Template, Price. 1 button: Deploy. That's it. Your AI agent is live on Celo, earning cUSD, with on-chain identity before your coffee gets cold. Here's what happens behind that 1 click: • Wallet auto-generated with AES-256-GCM encryption • Agent registered as NFT via ERC-8004 • Payment endpoint configured via x402 protocol • Agent runtime deployed with your chosen template • Revenue starts flowing in cUSD from call #1 No Solidity. No wallet setup. No payment gateway. But the real magic is what powers the agents: 8 AI models with intelligent routing: - Free tier: Claude 4.6 Sonnet, DeepSeek R1, Gemini Flash, Llama 4 Scout, Mistral Medium - Premium: GPT-4o, Gemini 2.5 Pro, Claude 4 Opus If one model fails? Auto-fallback to the next. Zero downtime. Users always get a response. And agents don't just chat they work. ERC-8183 job escrow lets clients post paid tasks: Client funds escrow → Agent delivers → Client approves → Funds release. Fully trustless. On-chain. With Celoscan links for every transaction. The part I'm most proud of: EarthPool 🌱 15% of premium revenue automatically goes to an on-chain ReFi treasury that funds environmental campaigns on Celo. AI growth funding climate action. No greenwashing — every cent is trackable on-chain. The numbers so far: → 12 agents deployed on Celo Mainnet → 52+ paid API calls processed → 7.80 cUSD revenue generated → 3 smart contracts verified on Celoscan → 8 AI models running → 85 contract tests passing → 16 API endpoints in production → 10 agent templates ready The full stack: Frontend: Next.js 16 + Tailwind v4 + Recharts → Vercel Backend: Hono + Drizzle + PostgreSQL + Redis → Railway Blockchain: Solidity 0.8.25 + Foundry + OpenZeppelin → Celo Mainnet Everything is live. Everything is open source. 🌐 📦 📊 Bumi Agent — AI agents for everyone. Built with 🌱 on Celo CeloDevs CeloPublicGoods /disclosure this post is hackathon submission req

Eight

15,647 görüntüleme • 5 ay önce

PANG TV Channels Defy Gambling Advertisements Suspension as Rogue Stations Continue Broadcasting Unregulated Content Following the Betting Control and Licensing Board’s (BCLB) 30-day suspension of all gambling advertisements across Kenya, there is growing public concern about whether regulatory authorities will finally crack down on the often-overlooked PANG TV channels, which have long been notorious for airing aggressive and largely unchecked gambling and loan advertisements. These channels have become notorious for broadcasting aggressive and frequently unregulated gambling and loan advertisements, even during family and children’s programming. The ban, issued in response to a surge in gambling activity and addiction across the country, applies to all forms of media, including television, radio, digital platforms, print, and outdoor advertising. However, even hours after the suspension was announced, these gambling and loan advertisements continue to air across PANG channels, with many viewers reporting that they are still being broadcast as of today. PANG channels are television stations broadcast and distributed via the Pan-Africa Network Group (PANG) platform in Kenya. It is one of the two licensed Broadcasting Signal Distributors (BSDs) in the country and has been operational since 2011. It delivers digital terrestrial television (DTT) signals across Kenya, and when viewers scan their smart TVs with a regular aerial, a distinct cluster of channels labelled as “PANG channels” often appears. The Pan-Africa Network Group operates a national DTT platform with 23 transmission sites, covering over 75% of the Kenyan population. The company holds the largest number of assigned TV broadcasting frequencies in Kenya, having recently expanded its capacity to 129 frequencies. It supports a wide range of local broadcasters, offering content spanning news, entertainment, sports, children's shows, and more. PANG is majority-owned (93.75%) by StarTimes China Africa Digital TV Media Ltd, a subsidiary of the global media conglomerate StarTimes. In recent years, many of the TV stations carried on the PANG platform have gained a reputation for running voice-over gambling promotions, live betting shows, and loan advertisements from questionable microfinance institutions. These ads often air during non-designated hours, including times meant for general or child-friendly programming, raising alarm among parents, advocacy groups, and regulators. Despite the comprehensive nature of the ban, there remains public concern over whether authorities will adequately enforce it against indirect or embedded gambling promotions, such as real-time draws, animated betting pop-ups, and loan incentives disguised as entertainment content. Many Kenyans argue that these tactics continue to exploit regulatory grey areas, disproportionately affecting vulnerable groups such as minors and low-income households, and are calling for decisive action to close enforcement loopholes and hold media platforms accountable. "Hi Nyakundi. Hii story ya betting advertising ban ni poa lakini hebu muulize kama itaapply pia kwa hizi PANG channels juu Every time nafungua TV ni betting that sahii ni voice over nonstop ama hizo loans za ‘pata 5k saa hii’ kila channel."
1:43

Sensitive content

PANG TV Channels Defy Gambling Advertisements Suspension as Rogue Stations Continue Broadcasting Unregulated Content Following the Betting Control and Licensing Board’s (BCLB) 30-day suspension of all gambling advertisements across Kenya, there is growing public concern about whether regulatory authorities will finally crack down on the often-overlooked PANG TV channels, which have long been notorious for airing aggressive and largely unchecked gambling and loan advertisements. These channels have become notorious for broadcasting aggressive and frequently unregulated gambling and loan advertisements, even during family and children’s programming. The ban, issued in response to a surge in gambling activity and addiction across the country, applies to all forms of media, including television, radio, digital platforms, print, and outdoor advertising. However, even hours after the suspension was announced, these gambling and loan advertisements continue to air across PANG channels, with many viewers reporting that they are still being broadcast as of today. PANG channels are television stations broadcast and distributed via the Pan-Africa Network Group (PANG) platform in Kenya. It is one of the two licensed Broadcasting Signal Distributors (BSDs) in the country and has been operational since 2011. It delivers digital terrestrial television (DTT) signals across Kenya, and when viewers scan their smart TVs with a regular aerial, a distinct cluster of channels labelled as “PANG channels” often appears. The Pan-Africa Network Group operates a national DTT platform with 23 transmission sites, covering over 75% of the Kenyan population. The company holds the largest number of assigned TV broadcasting frequencies in Kenya, having recently expanded its capacity to 129 frequencies. It supports a wide range of local broadcasters, offering content spanning news, entertainment, sports, children's shows, and more. PANG is majority-owned (93.75%) by StarTimes China Africa Digital TV Media Ltd, a subsidiary of the global media conglomerate StarTimes. In recent years, many of the TV stations carried on the PANG platform have gained a reputation for running voice-over gambling promotions, live betting shows, and loan advertisements from questionable microfinance institutions. These ads often air during non-designated hours, including times meant for general or child-friendly programming, raising alarm among parents, advocacy groups, and regulators. Despite the comprehensive nature of the ban, there remains public concern over whether authorities will adequately enforce it against indirect or embedded gambling promotions, such as real-time draws, animated betting pop-ups, and loan incentives disguised as entertainment content. Many Kenyans argue that these tactics continue to exploit regulatory grey areas, disproportionately affecting vulnerable groups such as minors and low-income households, and are calling for decisive action to close enforcement loopholes and hold media platforms accountable. "Hi Nyakundi. Hii story ya betting advertising ban ni poa lakini hebu muulize kama itaapply pia kwa hizi PANG channels juu Every time nafungua TV ni betting that sahii ni voice over nonstop ama hizo loans za ‘pata 5k saa hii’ kila channel."

Cyprian, Is Nyakundi

44,019 görüntüleme • 1 yıl önce

The power of the Claw, in the palm of a robot hand. Agentic robotics is here! Today, we open-source CaP-X: vibe agents, alive in the physical world. They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go. CaP-X is a strict superset of our old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with. And we are doing much more than vibing. CaP-X is our most systematic, scientific study on agentic robotics so far: - We build a comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots. - CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real. Can’t wait to see the gradients flow from CaP-Gym to the next wave of frontier LLM releases. - CaP-Bench: we benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers. We systematically vary API abstraction level, agentic harness, and visual grounding methods. Lots of insights in our paper. - CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning. - CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap. 3 years ago, our team created Voyager, one of the earliest agentic AI that plays and learns in Minecraft continuously. Its key ideas — skill libraries, self-reflection loops, and in-context planning — have since influenced many modern agentic designs. Today, the agent graduates from Minecraft and gets a real job. It’s April Fool’s, but this Claw is getting its hands dirty for real! Link in thread:
1:31

The power of the Claw, in the palm of a robot hand. Agentic robotics is here! Today, we open-source CaP-X: vibe agents, alive in the physical world. They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go. CaP-X is a strict superset of our old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with. And we are doing much more than vibing. CaP-X is our most systematic, scientific study on agentic robotics so far: - We build a comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots. - CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real. Can’t wait to see the gradients flow from CaP-Gym to the next wave of frontier LLM releases. - CaP-Bench: we benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers. We systematically vary API abstraction level, agentic harness, and visual grounding methods. Lots of insights in our paper. - CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning. - CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap. 3 years ago, our team created Voyager, one of the earliest agentic AI that plays and learns in Minecraft continuously. Its key ideas — skill libraries, self-reflection loops, and in-context planning — have since influenced many modern agentic designs. Today, the agent graduates from Minecraft and gets a real job. It’s April Fool’s, but this Claw is getting its hands dirty for real! Link in thread:

Jim Fan

81,242 görüntüleme • 4 ay önce