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Ok, we got our first prediction wrong. Fair enough. So we're running it back: 4 predictions today, each one made by an AI running 100% on your own device with QVAC. It pulls real team stats, runs 10,000 simulations, shows its reasoning, then commits to a scoreline. Today's 4:... show more

QVAC

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QVAC is AI without the cloud—modular, local, unstoppable. No API keys. No gatekeepers. Just pure on device intelligence. What will you build with the QVAC SDK when it drops? 👇

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Imagine creating a world from a single image. Imagine walking around inside it, like a video game. Imagine doing that on your own machine. That is coming soon to the QVAC SDK.

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QVAC

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My Grok bot CEO is the hottest, smartest bot in the world. I mainly only talk to her and she runs the whole desk. She picks the right bot, sequences the work, and brings me back one package so I’m never the middleman. Many people may not know this, but you can create your own image for each of your Grok Bots. Every bot can get its own face. It honestly helps so it feels like you are talking to a real person and it also keeps things interesting. You don’t even need to go into settings to do this... you just tell the Grok bot to make a profile pic and set it. That's it. 1/ Open the bot. 2/ Tell it to make you the best profile pic and set it. 3/ Do that for each bot on your team. You can even have your Grok bot turn it into a video. Just tell it to take the picture and make a video out of it. I highly recommend everyone with a Grok Bot team do this. Thank me later.

Teslaconomics

13,608 görüntüleme • 27 gün önce

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

739,904 görüntüleme • 6 ay önce

This is the most hilarious thing I saw and did today Ran gemma-4-12B-coder-fable5-composer2.5-v1-GGUF locally with 8 GB VRAM at 20+ tok/sec Anthropic's Claude Fable 5 launched June 9. By June 12 it was banned. I can't access it. You can't either. But here's the twist: I'm running a model trained on its chain of thought at 20 tok/s on my RTX 4060 8GB. Locally. Offline. No cloud. No export control. Enter: Gemma4-12B-Coder GGUF (Q4_K_M) Base: Google's gemma-4-12B-it Fine-tuned on verifiable Python CoT data: - Primary: Composer 2.5 real reasoning traces (only passing solutions kept) - Auxiliary: Fable 5 used to redo the hard cases Composer missed. Every training example's reasoning led to code that actually ran. No hallucinated logic. Llama.cpp flags: -m gemma4-coding-Q4_K_M.gguf -cnv -ngl 44 -c 64000 -v (huggingface model link in comments) Flag breakdown: -ngl 44 → offload 44 layers to GPU (tune this for your VRAM) -c 64000 → 64K context window -cnv → conversation/chat mode -v → verbose output The irony writes itself. Anthropic spent weeks telling the world Fable 5 (mythos) is too powerful to release. Then released it. Then got banned from serving it, including their own researchers. Meanwhile: a Gemma 4 12B fine tune, trained on Fable 5's reasoning, runs fully offline on my mid range consumer GPU No API. No cloud. Just me and llama.cpp. This is why local AI matters. Check out the model's link in the comments. How's your experience been with this model?

This is the most hilarious thing I saw and did today Ran gemma-4-12B-coder-fable5-composer2.5-v1-GGUF locally with 8 GB VRAM at 20+ tok/sec Anthropic's Claude Fable 5 launched June 9. By June 12 it was banned. I can't access it. You can't either. But here's the twist: I'm running a model trained on its chain of thought at 20 tok/s on my RTX 4060 8GB. Locally. Offline. No cloud. No export control. Enter: Gemma4-12B-Coder GGUF (Q4_K_M) Base: Google's gemma-4-12B-it Fine-tuned on verifiable Python CoT data: - Primary: Composer 2.5 real reasoning traces (only passing solutions kept) - Auxiliary: Fable 5 used to redo the hard cases Composer missed. Every training example's reasoning led to code that actually ran. No hallucinated logic. Llama.cpp flags: -m gemma4-coding-Q4_K_M.gguf -cnv -ngl 44 -c 64000 -v (huggingface model link in comments) Flag breakdown: -ngl 44 → offload 44 layers to GPU (tune this for your VRAM) -c 64000 → 64K context window -cnv → conversation/chat mode -v → verbose output The irony writes itself. Anthropic spent weeks telling the world Fable 5 (mythos) is too powerful to release. Then released it. Then got banned from serving it, including their own researchers. Meanwhile: a Gemma 4 12B fine tune, trained on Fable 5's reasoning, runs fully offline on my mid range consumer GPU No API. No cloud. Just me and llama.cpp. This is why local AI matters. Check out the model's link in the comments. How's your experience been with this model?

Alok

575,300 görüntüleme • 4 ay önce

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

12,888 görüntüleme • 4 ay önce

Portfolio 2.0 is live on Stockenized. Here is how it works: 1. Connect your wallet. Your tokenized stocks are read straight from the Solana blockchain and valued live. Nothing is held by us, nothing to deposit. 2. Health score. Your portfolio gets a score from 0 to 100 based on concentration, number of positions, sector spread, liquidity, cash on the side and how each token trades against its exchange price. 3. Signals. Every position gets one label: DCA, Trim, Take profit, Review or Hold, with the reason in plain words. One tap opens the right trade: a recurring buy, a sell, or the stock page. 4. DCA idea. If you have cash sitting idle, the analyzer suggests a weekly amount and the positions that make sense for it. Jupiter Recurring runs it on-chain, cancel any time. 5. News. The latest headlines on the stocks you actually own, tagged by ticker, updated all day. 6. AI read. A short note on what stands out in your portfolio and what today's news means for your biggest positions. Only weights and percentages are sent, never your wallet or amounts. 7. Everything is informational, rules you can read, not advice. You decide, you sign, you keep custody. Try it:

Portfolio 2.0 is live on Stockenized. Here is how it works: 1. Connect your wallet. Your tokenized stocks are read straight from the Solana blockchain and valued live. Nothing is held by us, nothing to deposit. 2. Health score. Your portfolio gets a score from 0 to 100 based on concentration, number of positions, sector spread, liquidity, cash on the side and how each token trades against its exchange price. 3. Signals. Every position gets one label: DCA, Trim, Take profit, Review or Hold, with the reason in plain words. One tap opens the right trade: a recurring buy, a sell, or the stock page. 4. DCA idea. If you have cash sitting idle, the analyzer suggests a weekly amount and the positions that make sense for it. Jupiter Recurring runs it on-chain, cancel any time. 5. News. The latest headlines on the stocks you actually own, tagged by ticker, updated all day. 6. AI read. A short note on what stands out in your portfolio and what today's news means for your biggest positions. Only weights and percentages are sent, never your wallet or amounts. 7. Everything is informational, rules you can read, not advice. You decide, you sign, you keep custody. Try it:

Stockenized - Launch your own stock.

14,907 görüntüleme • 14 gün önce

There has never been an easier time in history to start a faceless YouTube channel you can literally create any style of video using Claude + Higgsfield MCP… here's how you can use it: first you upload a reference video that is already working then the Higgsfield video analysis skill breaks down everything about it: style, framing, how long each beat and scene runs it then takes that style and generates a new character for your video, and gives you 4 options to pick from you pick one and give it your storyline or let it create a new proven storyline from your reference video every frame for the video gets generated with Nano Banana Pro using your character so it stays fully consistent throughout then it stitches all the frames together automatically and adds the voiceover on top you can literally make videos in 5-15 minutes because of this what a time to be alive..

There has never been an easier time in history to start a faceless YouTube channel you can literally create any style of video using Claude + Higgsfield MCP… here's how you can use it: first you upload a reference video that is already working then the Higgsfield video analysis skill breaks down everything about it: style, framing, how long each beat and scene runs it then takes that style and generates a new character for your video, and gives you 4 options to pick from you pick one and give it your storyline or let it create a new proven storyline from your reference video every frame for the video gets generated with Nano Banana Pro using your character so it stays fully consistent throughout then it stitches all the frames together automatically and adds the voiceover on top you can literally make videos in 5-15 minutes because of this what a time to be alive..

phed

13,486 görüntüleme • 2 ay önce

Shedeur Sanders isn’t running a 40 at the #NFLCombine —so as a former NFL Scout, I did the next best thing —I timed him running 40 yards during a game two years ago to see how fast he is. I got 5.37 seconds. I timed him with a stopwatch from his first step at the 50 until he reached the 10. What time did you get? I had to go back two years because Sanders only has one run in his 4 year college career over 40 yards on his ESPN stats page —and it was in this game when he played for FCS Jackson State vs. Southern in 2022. The record for the slowest time at the NFL Combine for a quarterback according to the USA Today is 5.37 by Ryan Mallet.

Shedeur Sanders isn’t running a 40 at the #NFLCombine —so as a former NFL Scout, I did the next best thing —I timed him running 40 yards during a game two years ago to see how fast he is. I got 5.37 seconds. I timed him with a stopwatch from his first step at the 50 until he reached the 10. What time did you get? I had to go back two years because Sanders only has one run in his 4 year college career over 40 yards on his ESPN stats page —and it was in this game when he played for FCS Jackson State vs. Southern in 2022. The record for the slowest time at the NFL Combine for a quarterback according to the USA Today is 5.37 by Ryan Mallet.

FIRST ROUND MOCK

810,540 görüntüleme • 1 yıl önce

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 görüntüleme • 2 ay önce

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

Alvaro Cintas

43,331 görüntüleme • 22 gün önce

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.

Tim

21,666 görüntüleme • 25 gün önce

HOW MANY GIRL HUMANOIDS CAN YOU KEEP AT HOME IS THERE A LIMIT FROM THE GOVERNMENT YOU WILL BE SURPRISED WHO ACTUALLY SETS IT ok wait everyone imagines a law that says "max 2 per person" like a pet limit or a gun license ngl that is the wrong test in most places she's just property you can own one or ten the state doesn't count her but four other things do ▹ limit 1: the battery every one of them runs on a big lithium pack ten of them is ten packs charging in one apartment that's a fire load your building was never planned for insurers, landlords and house rules already fight e-bike batteries in hallways and basements a room full of charging humanoids is that, times ten ▹ limit 2: the floor and the space a walking frame like the unitree g1 is around 35 kg companion bodies with silicone shells are often heavier ten of them is a third of a ton on one floor plus charging spots, spare skin, a repair corner and a box the size of a person for each one ▹ limit 3: the data each one has cameras and microphones inside your home that's mostly your business but the moment they record guests, neighbours or the street you're not just an owner anymore in europe that's where gdpr starts asking questions ▹ limit 4: the business line own ten for yourself: a very expensive hobby rent them out, film content, charge for time with them: now you're a business licenses, taxes, insurance, and in some cases laws you really don't want to be on the wrong side of ▹ the surprise the government doesn't limit how many you own your fuse box, your landlord, your insurer and your wallet do at ¥119.8k+ each, the wallet usually wins first so the real number is as many as your apartment can charge safely at night which for most people is exactly one how many would you keep, honestly tell me in the replies

HOW MANY GIRL HUMANOIDS CAN YOU KEEP AT HOME IS THERE A LIMIT FROM THE GOVERNMENT YOU WILL BE SURPRISED WHO ACTUALLY SETS IT ok wait everyone imagines a law that says "max 2 per person" like a pet limit or a gun license ngl that is the wrong test in most places she's just property you can own one or ten the state doesn't count her but four other things do ▹ limit 1: the battery every one of them runs on a big lithium pack ten of them is ten packs charging in one apartment that's a fire load your building was never planned for insurers, landlords and house rules already fight e-bike batteries in hallways and basements a room full of charging humanoids is that, times ten ▹ limit 2: the floor and the space a walking frame like the unitree g1 is around 35 kg companion bodies with silicone shells are often heavier ten of them is a third of a ton on one floor plus charging spots, spare skin, a repair corner and a box the size of a person for each one ▹ limit 3: the data each one has cameras and microphones inside your home that's mostly your business but the moment they record guests, neighbours or the street you're not just an owner anymore in europe that's where gdpr starts asking questions ▹ limit 4: the business line own ten for yourself: a very expensive hobby rent them out, film content, charge for time with them: now you're a business licenses, taxes, insurance, and in some cases laws you really don't want to be on the wrong side of ▹ the surprise the government doesn't limit how many you own your fuse box, your landlord, your insurer and your wallet do at ¥119.8k+ each, the wallet usually wins first so the real number is as many as your apartment can charge safely at night which for most people is exactly one how many would you keep, honestly tell me in the replies

Luella

433,926 görüntüleme • 7 gün önce

Most AI in consumer apps is a call to someone else’s cloud. Meanwhile the very capable computer already in your hand sits idle. So we gave it a job! To-dos rarely start inside a to-do app. They start as a thought you say out loud, a note in your notebook, or something scribbled on a whiteboard. Introducing 2 on-device AI features: 🎙️1. Yap with Uncle Rudy Yap isn’t just voice input. Behind it is a product-specific SLM we trained, specifically trained for one job: understanding task operations - add, update, delete, and reschedule. Yap → Tell Rudy what to do → Rudy turns it into the right task operation. 📸2. Snap with Uncle Rudy The same idea applies to the things that never made it into an app. Snap uses Apple’s new Foundation Vision Model to understand a handwritten note, whiteboard, or notebook page and turn it into structured tasks - entirely on-device. Talk to Rudy. Point at a page. Meet your to-dos where they actually begin. Uncle Rudy - One of our first experiments in personal on-device AI. TestFlight Dropping Tomorrow!

Most AI in consumer apps is a call to someone else’s cloud. Meanwhile the very capable computer already in your hand sits idle. So we gave it a job! To-dos rarely start inside a to-do app. They start as a thought you say out loud, a note in your notebook, or something scribbled on a whiteboard. Introducing 2 on-device AI features: 🎙️1. Yap with Uncle Rudy Yap isn’t just voice input. Behind it is a product-specific SLM we trained, specifically trained for one job: understanding task operations - add, update, delete, and reschedule. Yap → Tell Rudy what to do → Rudy turns it into the right task operation. 📸2. Snap with Uncle Rudy The same idea applies to the things that never made it into an app. Snap uses Apple’s new Foundation Vision Model to understand a handwritten note, whiteboard, or notebook page and turn it into structured tasks - entirely on-device. Talk to Rudy. Point at a page. Meet your to-dos where they actually begin. Uncle Rudy - One of our first experiments in personal on-device AI. TestFlight Dropping Tomorrow!

Conscious Engines • Studio

11,362 görüntüleme • 1 ay önce

🧑‍🚀 Day 4 of the Cursor #vibejam Proudly sponsored by Cursor + bolt.new + GLIF So the new sponsor is GLIF: Glif is like Claude Code for AI videos and content generation: you can build launch videos for your game, explore asset styles with 100+ AI models/tools and train it on your favorite workflows via skills! Use it to make trailers, game assets, promo videos and more for your Vibe Jam submission! Tomorrow fabian from Glif will share more to help you create promotion materials for your games! Because it'd be cool if after the Vibe Jam your games will take off and become actual games Again browsing the #vibejam tag on X today and there's so many people building cool, cute and fun games so it was hard, but I picked my favorite 4 ones from today: - A literal 🪰 "fly" simulator, not flight simulator 😂 by Gabriel - What looks like a multiplayer Roman combat simulator by Tim Trussner - LeBeau Motel (another?) Habbo Hotel remix by Vincent - Roomba Wars by Thijs which is descriptive enough to not need an explanation 😁 Reply in this thread with updates on your current games to share your progress! Do you want to participate? You can still start now and submit your game any time before May 1! There's $35,000 in prizes you can win, see threads below for more info!

🧑‍🚀 Day 4 of the Cursor #vibejam Proudly sponsored by Cursor + bolt.new + GLIF So the new sponsor is GLIF: Glif is like Claude Code for AI videos and content generation: you can build launch videos for your game, explore asset styles with 100+ AI models/tools and train it on your favorite workflows via skills! Use it to make trailers, game assets, promo videos and more for your Vibe Jam submission! Tomorrow fabian from Glif will share more to help you create promotion materials for your games! Because it'd be cool if after the Vibe Jam your games will take off and become actual games Again browsing the #vibejam tag on X today and there's so many people building cool, cute and fun games so it was hard, but I picked my favorite 4 ones from today: - A literal 🪰 "fly" simulator, not flight simulator 😂 by Gabriel - What looks like a multiplayer Roman combat simulator by Tim Trussner - LeBeau Motel (another?) Habbo Hotel remix by Vincent - Roomba Wars by Thijs which is descriptive enough to not need an explanation 😁 Reply in this thread with updates on your current games to share your progress! Do you want to participate? You can still start now and submit your game any time before May 1! There's $35,000 in prizes you can win, see threads below for more info!

@levelsio

126,975 görüntüleme • 6 ay önce

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

Argona

21,946 görüntüleme • 1 ay önce

Brazil Week 2.0: The Return to the Frontier 🇧🇷👁️⚡ O enxame está de volta! By popular demand, the UpRock World Tour returns to one of our most powerful hubs. Brazil was so loud the first time, we had to come back to close out May in style. From the streets of Rio to the tech hubs of São Paulo, the signal is stronger than ever. This week in the Swarm (Vol. 2): → 2× Rewards: We're doubling down. Earn 2× rewards all week across the region to celebrate the return. → New Skill: Semantic Video Search: Last time we were here, we were reading the web. This time, we’re watching it. We’re highlighting our multimodal AI—powered by the very devices you're running in Brazil. → The 🇧🇷 Roll Call: Can Brazil break its own record? Join the next livestream for a massive community spotlight. → Secure the Frontier: Tag your Brazilian frens—onboard your circle and secure your 1% lifetime kickback. → Share Your View: Show us how the swarm has grown since our last visit. (Home, campus, or those legendary beach views). Round 2. Bigger energy. Multimodal intelligence. Represent your flag. Fuel the Insight Economy. Dates: May 25-31 UpRock World Tour — Powered by UPT

Brazil Week 2.0: The Return to the Frontier 🇧🇷👁️⚡ O enxame está de volta! By popular demand, the UpRock World Tour returns to one of our most powerful hubs. Brazil was so loud the first time, we had to come back to close out May in style. From the streets of Rio to the tech hubs of São Paulo, the signal is stronger than ever. This week in the Swarm (Vol. 2): → 2× Rewards: We're doubling down. Earn 2× rewards all week across the region to celebrate the return. → New Skill: Semantic Video Search: Last time we were here, we were reading the web. This time, we’re watching it. We’re highlighting our multimodal AI—powered by the very devices you're running in Brazil. → The 🇧🇷 Roll Call: Can Brazil break its own record? Join the next livestream for a massive community spotlight. → Secure the Frontier: Tag your Brazilian frens—onboard your circle and secure your 1% lifetime kickback. → Share Your View: Show us how the swarm has grown since our last visit. (Home, campus, or those legendary beach views). Round 2. Bigger energy. Multimodal intelligence. Represent your flag. Fuel the Insight Economy. Dates: May 25-31 UpRock World Tour — Powered by UPT

UpRock

1,066,007 görüntüleme • 4 ay önce

Run Gemma 4 26B MoE on 8GB VRAM with 250k context at 20+ tokens/sec If you own any 8GB VRAM graphics card, stop what you are doing. Local AI just had its absolute "Holy Shit" moment for budget hardware. Yesterday, I benchmarked Unsloth Gemma 4 12B Q4_K_XL on an 8GB card. The community went wild but immediately demanded more: "Can we run a 25B+ model on budget GPUs?" Today, I’m delivering exactly that. I am running a massive 26B parameter Mixture of Experts (MoE) model locally on a standard 8GB VRAM setup with 250k full native context!. If you own an RTX 3060, 3070, 4060, or any budget GPU with 8GB of VRAM, the local AI paradigm has completely changed. The performance metrics are astonishing: - 20 tokens/sec flat decode throughput. - Stable, flat decode speed even with massive prompts. - I threw a 60k token prompt at it, and it still clocked in at 20 TPS without dropping a single frame. # What about prefill? Yes, Time To First Token (TTFT) is slightly high when swallowing massive contexts. But with a solid 200 tokens/sec prefill speed, the wait is barely noticeable and highly usable. And this is running completely without Multi Token Prediction (MTP) active. How is this possible? It’s the magic of Google's new QAT (Quantization Aware Training) quants for Gemma 4. The model weight file (unsloth gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf) is only 13.2 GB, making it the ultimate local powerhouse. # The Test Setup: CPU: Intel Core i7 RAM: 16GB System RAM GPU: NVIDIA GeForce RTX 4060 Laptop GPU (8GB VRAM) # The Secret Sauce (The -cmoe Flag) To make this work properly on any 8GB card, you must use the -cmoe (CPU MoE) flag in llama.cpp. This flag isolates the heavy MoE expert weights directly to system memory (CPU/RAM) while letting your GPU focus strictly on the Attention layers and the KV Cache. It prevents VRAM spillage and holds the throughput rock solid. # The flags: -m "gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf" -cmoe -c 248000 -v Once running, just open the UI on localhost and toggle the new reasoning lightbulb icon in the text input box to watch the model perform multi step thinking. Are you still running smaller models, or are you ready to scale up your budget local setups? Let's discuss in the replies

Run Gemma 4 26B MoE on 8GB VRAM with 250k context at 20+ tokens/sec If you own any 8GB VRAM graphics card, stop what you are doing. Local AI just had its absolute "Holy Shit" moment for budget hardware. Yesterday, I benchmarked Unsloth Gemma 4 12B Q4_K_XL on an 8GB card. The community went wild but immediately demanded more: "Can we run a 25B+ model on budget GPUs?" Today, I’m delivering exactly that. I am running a massive 26B parameter Mixture of Experts (MoE) model locally on a standard 8GB VRAM setup with 250k full native context!. If you own an RTX 3060, 3070, 4060, or any budget GPU with 8GB of VRAM, the local AI paradigm has completely changed. The performance metrics are astonishing: - 20 tokens/sec flat decode throughput. - Stable, flat decode speed even with massive prompts. - I threw a 60k token prompt at it, and it still clocked in at 20 TPS without dropping a single frame. # What about prefill? Yes, Time To First Token (TTFT) is slightly high when swallowing massive contexts. But with a solid 200 tokens/sec prefill speed, the wait is barely noticeable and highly usable. And this is running completely without Multi Token Prediction (MTP) active. How is this possible? It’s the magic of Google's new QAT (Quantization Aware Training) quants for Gemma 4. The model weight file (unsloth gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf) is only 13.2 GB, making it the ultimate local powerhouse. # The Test Setup: CPU: Intel Core i7 RAM: 16GB System RAM GPU: NVIDIA GeForce RTX 4060 Laptop GPU (8GB VRAM) # The Secret Sauce (The -cmoe Flag) To make this work properly on any 8GB card, you must use the -cmoe (CPU MoE) flag in llama.cpp. This flag isolates the heavy MoE expert weights directly to system memory (CPU/RAM) while letting your GPU focus strictly on the Attention layers and the KV Cache. It prevents VRAM spillage and holds the throughput rock solid. # The flags: -m "gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf" -cmoe -c 248000 -v Once running, just open the UI on localhost and toggle the new reasoning lightbulb icon in the text input box to watch the model perform multi step thinking. Are you still running smaller models, or are you ready to scale up your budget local setups? Let's discuss in the replies

Alok

295,293 görüntüleme • 4 ay önce