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Inferam

2,270,298 subscribers

18,764,798 次观看 • 2 年前 •via X (Twitter)

动物喜剧健康养生

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Interesting things

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Learn Something

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🚨Improve YOUR🫵PC with this Optimization🚨 ✅Disable Telemetry, Advertising, Data Collection and other unnecessary things hogging your CPU. 📌More useful information in my Discord Server where I talk about how to ACTUALLY Lower Ping and more. ❤️+♻️

🚨Improve YOUR🫵PC with this Optimization🚨 ✅Disable Telemetry, Advertising, Data Collection and other unnecessary things hogging your CPU. 📌More useful information in my Discord Server where I talk about how to ACTUALLY Lower Ping and more. ❤️+♻️

Latenncy

106,274 次观看 • 7 个月前

I have found it really useful to convert notes/essays/paper into Artifacts. And it seems Fable 5 is really good at it. Artifacts are great for building deeper intuition on any topic. Just shared this one I did on Satya's latest essay on the Reverse Information Paradox.

I have found it really useful to convert notes/essays/paper into Artifacts. And it seems Fable 5 is really good at it. Artifacts are great for building deeper intuition on any topic. Just shared this one I did on Satya's latest essay on the Reverse Information Paradox.

elvis

14,270 次观看 • 1 个月前

There are a LOT of amazing "pose reference packs" for artists. I love them. Wide variety of people and a wide variety of costumes etc. Incredibly useful for drawing from. Also, highly recommended to draw from still frames of whatever actiony videos you can find. Poses are great - but there's information in movement too. Here's one that's fun - by Elsaucepapii on Facebook. Set it to half speed. Scroll it. Stop on a frame. Draw.

There are a LOT of amazing "pose reference packs" for artists. I love them. Wide variety of people and a wide variety of costumes etc. Incredibly useful for drawing from. Also, highly recommended to draw from still frames of whatever actiony videos you can find. Poses are great - but there's information in movement too. Here's one that's fun - by Elsaucepapii on Facebook. Set it to half speed. Scroll it. Stop on a frame. Draw.

Wetterschneider

42,071 次观看 • 7 个月前

The #CoreIgnitionDrop Season 1 is ending today, September 11th at 18:29 UTC and Season 2 is starting tomorrow, September 12th at 00:00 UTC🔥 Here is some useful information: 🔸Season 1 rewards will remain claimable (for 6 months as previously announced); 🔸Season 1 badges and invitees will be carried over to Season 2; 🔸All Season 1 participants who don't have an invite code will have one once logged in for Season 2; 🔸Sparks will be reset to 0 when Season 2 starts; 🔸The 3rd claiming phase for Season 1 will take place after Season 2 starts. Stay tuned and get ignited! 🔗

The #CoreIgnitionDrop Season 1 is ending today, September 11th at 18:29 UTC and Season 2 is starting tomorrow, September 12th at 00:00 UTC🔥 Here is some useful information: 🔸Season 1 rewards will remain claimable (for 6 months as previously announced); 🔸Season 1 badges and invitees will be carried over to Season 2; 🔸All Season 1 participants who don't have an invite code will have one once logged in for Season 2; 🔸Sparks will be reset to 0 when Season 2 starts; 🔸The 3rd claiming phase for Season 1 will take place after Season 2 starts. Stay tuned and get ignited! 🔗

Core DAO 🔶

38,898 次观看 • 2 年前

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.

Igor Kulakov

18,773 次观看 • 1 年前

A Nairobi man is seeking help in tracing two women suspected of drugging and robbing him of valuables worth over Ksh 700,000 after he invited the suspects to his residence following a night out last weekend in Ongata Rongai, Kajiado County. According to available information and CCTV footage, one of the women was seen stepping outside to conduct what appears to be surveillance, while her accomplice allegedly packed the stolen items inside the house. The suspects made away with a silver Apple MacBook Pro 16-inch, an iPhone 14 Pro Max, an iPod Pro Max, a pair of Apple AirPods Max headphones, and KSh 52,000 which was transferred from the victim’s M-PESA account to a phone number that has since been switched off. The footage shows the two women leaving the premises at around 2:55 PM on Saturday while carrying bags believed to contain the stolen items. The victim was reportedly unconscious inside the apartment at the time of the theft. He is now appealing to the public for assistance in identifying the two suspects, hoping that the CCTV footage will prompt anyone with useful information to come forward.

A Nairobi man is seeking help in tracing two women suspected of drugging and robbing him of valuables worth over Ksh 700,000 after he invited the suspects to his residence following a night out last weekend in Ongata Rongai, Kajiado County. According to available information and CCTV footage, one of the women was seen stepping outside to conduct what appears to be surveillance, while her accomplice allegedly packed the stolen items inside the house. The suspects made away with a silver Apple MacBook Pro 16-inch, an iPhone 14 Pro Max, an iPod Pro Max, a pair of Apple AirPods Max headphones, and KSh 52,000 which was transferred from the victim’s M-PESA account to a phone number that has since been switched off. The footage shows the two women leaving the premises at around 2:55 PM on Saturday while carrying bags believed to contain the stolen items. The victim was reportedly unconscious inside the apartment at the time of the theft. He is now appealing to the public for assistance in identifying the two suspects, hoping that the CCTV footage will prompt anyone with useful information to come forward.

Cyprian, Is Nyakundi

157,364 次观看 • 1 年前

Trump is right to call Kristen Welker out. If she will lie about his endorsement record, she will lie about anything. Bill Maher already corrected himself on air after Trump sent him the numbers. Maher is a comedian. Welker is supposed to be a news anchor. Calling a mid-90s win rate “mixed results” is not a slip. It is a narrative. The press has done this for decades. Soften every win. Inflate every loss. Then act shocked when people stop trusting the product. Calling that out is one of the most useful things Trump does. Accurate information is not optional. A country that lets anchors rewrite the scoreboard on purpose is not informing the public. It is managing it. Report it to the FCC. Fine the network. Make the lie expensive.

Trump is right to call Kristen Welker out. If she will lie about his endorsement record, she will lie about anything. Bill Maher already corrected himself on air after Trump sent him the numbers. Maher is a comedian. Welker is supposed to be a news anchor. Calling a mid-90s win rate “mixed results” is not a slip. It is a narrative. The press has done this for decades. Soften every win. Inflate every loss. Then act shocked when people stop trusting the product. Calling that out is one of the most useful things Trump does. Accurate information is not optional. A country that lets anchors rewrite the scoreboard on purpose is not informing the public. It is managing it. Report it to the FCC. Fine the network. Make the lie expensive.

Paul A. Szypula 🇺🇸

13,261 次观看 • 7 天前

My Grok Bot + Kimi K3 Made $2,200 While I Was Away I gave them the job, closed the laptop, and came back to this agents were doing the research, checking the numbers, passing work to each other, and Kimi K3 was deciding what needed to happen next I wasn’t sitting there moving results from one chat to another, I wasn’t writing the next prompt, and I wasn’t the guy keeping everything moving RESEARCH was finding the information ANALYSIS was turning it into something useful VERIFY was catching bad numbers and old data KIMI K3 was keeping the whole thing together The crazy part is that I wasn’t even there I set the goal, the agents took the work, and the system kept going while I was away $2,200 later, I came back to the result Maybe the biggest upgrade with AI isn’t making it smarter Maybe it’s finally getting it to stop waiting for us to tell it what to do next

My Grok Bot + Kimi K3 Made $2,200 While I Was Away I gave them the job, closed the laptop, and came back to this agents were doing the research, checking the numbers, passing work to each other, and Kimi K3 was deciding what needed to happen next I wasn’t sitting there moving results from one chat to another, I wasn’t writing the next prompt, and I wasn’t the guy keeping everything moving RESEARCH was finding the information ANALYSIS was turning it into something useful VERIFY was catching bad numbers and old data KIMI K3 was keeping the whole thing together The crazy part is that I wasn’t even there I set the goal, the agents took the work, and the system kept going while I was away $2,200 later, I came back to the result Maybe the biggest upgrade with AI isn’t making it smarter Maybe it’s finally getting it to stop waiting for us to tell it what to do next

leopardracer

16,285 次观看 • 9 天前

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,314 次观看 • 4 个月前

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

12,177 次观看 • 26 天前

Grok Bot is not just another chatbot. It is an always-on AI teammate with its own computer. It can sign in to your tools, work across apps, inboxes and websites, keep working 24/7 and return with finished work. Here are 100 useful tasks you could give Grok Bot: Sales: • Research companies, customers, leads and competitors • Build targeted lists of potential customers • Find and score high-intent sales prospects • Enrich contact details and company records • Draft personalized emails and LinkedIn messages • Turn call transcripts into CRM notes and follow-ups • Prepare detailed account briefs before meetings • Keep CRM records and customer details updated • Flag stalled deals, risks and overdue next steps • Prepare weekly sales reports and pipeline scoreboards Marketing and content: • Research content ideas and trending topics • Prepare marketing campaign briefs • Create and manage campaigns across different tools • Research competitors’ marketing strategies • Build weekly or monthly content calendars • Repurpose content for social media, email and other platforms • Draft social posts, newsletters and blog content • Create multiple versions of advertisements and copy • Monitor brand mentions and prepare responses • Prepare campaign performance reports Inbox and administration: • Monitor your inbox and organize emails • Draft replies in your preferred writing style • Create a daily summary of important messages • Extract tasks, deadlines and action items from emails • Follow up on stalled conversations and forgotten tasks • Prepare meeting agendas and discussion points • Turn meeting notes into tasks and follow-ups • Organize email attachments and cloud files • Enter information into forms and internal systems • Update project trackers, documents and dashboards Finance and operations: • Process invoices received through Gmail • Extract invoice information automatically • Match invoices with purchase orders • Track expenses and prepare finance updates • Organize receipts and expense claims • Clean and reconcile financial spreadsheets • Compare vendor prices and prepare recommendations • Monitor software subscriptions and renewals • Track inventory and flag low supplies • Handle repetitive office operations Hiring and HR: • Help onboard new employees • Organize seating, accounts and equipment for new hires • Draft job descriptions • Research and identify potential candidates • Organize applications and candidate information • Coordinate interview schedules • Prepare candidate briefs before interviews • Combine interview feedback into one report • Create training materials and onboarding guides • Prepare employee offboarding checklists Customer support and success: • Monitor incoming customer requests • Create and categorize support tickets • Prioritize urgent customer issues • Draft customer support replies • Search internal documentation for answers • Escalate important problems to the correct team • Send product updates to customers • Keep follow-up tasks synchronized across tools • Prepare customer health and renewal reports • Turn customer feedback into clear themes Product and engineering: • Prepare and test product demo environments • Fix stale demo data and create readiness checklists • Reproduce software bugs inside the product interface • File detailed bug reports with steps and evidence • Hand bugs to another debugging Bot to work on fixes • Test websites and apps for common problems • Run repetitive quality-assurance checks • Test logins, forms, buttons and checkout flows • Update project boards and release notes • Turn user feedback into organized feature requests Research and reporting: • Conduct detailed web research • Collect sources and prepare cited summaries • Compare competitors’ products and pricing • Monitor industry news and important announcements • Organize research findings into tables • Clean and analyze large spreadsheets • Create daily, weekly or monthly reports • Update internal dashboards with new information • Prepare presentation outlines and meeting briefs • Summarize long documents, emails and transcripts E-commerce and online business: • Update product listings across platforms • Check inventory and identify low-stock products • Monitor competitors’ prices • Analyze customer reviews and common complaints • Track delayed or problematic orders • Prepare return and refund cases for approval • Follow up with vendors about missing orders • Create promotional campaign drafts • Clean product catalogs and remove outdated information • Prepare daily store performance summaries Events and project management: • Research venues, vendors and service providers • Compare quotes and prepare options • Create event checklists and timelines • Coordinate invitations and attendee lists • Track project deadlines and responsibilities • Remind team members about overdue work • Collect progress updates from different tools • Prepare weekly project status reports • Identify blockers before they delay a project • Keep everyone aligned on the latest information Bots, routines and automation: • Watch you complete a workflow and learn how you do it • Save workflows as reusable routines • Repeat multi-step tasks without being taught again • Learn your voice, preferences and edge cases • Manage several specialist Bots simultaneously • Let Bots communicate and share information • Place Bots in group chats to coordinate work • Use one chief-of-staff Bot to manage the others • Run research, reporting and other jobs overnight • Ask for your approval only when a decision requires you You can message Grok Bot like a coworker, teach it how you prefer things done and let it improve over time. The real value is not just getting an answer. It is handing off an entire job and getting finished work back.

DogeDesigner

83,462 次观看 • 26 天前