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I made this for Sentry Hackweek. I'll be doing a deep dive soon, but it's a personal recorder that sends my transcripts to a router that determines if the text is a simply a todo, reminder OR if it's work. If it's work, Pi agents get started in it... show more

Scott Tolinski

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99,722 просмотров • 4 дней назад •via X (Twitter)

Искусство Наука и технологии Образование

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I don’t have a ton of videos but here’s a couple short clips people took. Didn’t want a music career or anything just wanted to make some money doing something positive and not having to work for others. Ended up doing pretty well at it and having a lot of fun memories. Thanks for all the new followers also. Didn’t expect that.

I don’t have a ton of videos but here’s a couple short clips people took. Didn’t want a music career or anything just wanted to make some money doing something positive and not having to work for others. Ended up doing pretty well at it and having a lot of fun memories. Thanks for all the new followers also. Didn’t expect that.

Taylor Silverman

133,588 просмотров • 1 год назад

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 просмотров • 3 месяцев назад

Well, time for for work. I hope everyone has a good night. Sweet dreams...... PS: I don't know if this is AI or not. Even if it is, it's still 🔥.

Well, time for for work. I hope everyone has a good night. Sweet dreams...... PS: I don't know if this is AI or not. Even if it is, it's still 🔥.

Paul

141,483 просмотров • 18 дней назад

Man who owns a car and is wearing a gold chain is worried that if he has to get a job they are going to cut his food stamps “So if I get a job, I can get food stamps, but if I don't get a job, they cut my food stamps. But if I get a job, they're gonna cut my food stamps anyway. How does that work?” The only new requirement for food stamps requirements is working or volunteering 20 hours per week if you are able bodied If you don’t meet this you’re limited to 3 months of SNAP benefits in a 36-month period Working or volunteering 20 hours a week so taxpayers pay for your food shouldn’t be a big problem

Man who owns a car and is wearing a gold chain is worried that if he has to get a job they are going to cut his food stamps “So if I get a job, I can get food stamps, but if I don't get a job, they cut my food stamps. But if I get a job, they're gonna cut my food stamps anyway. How does that work?” The only new requirement for food stamps requirements is working or volunteering 20 hours per week if you are able bodied If you don’t meet this you’re limited to 3 months of SNAP benefits in a 36-month period Working or volunteering 20 hours a week so taxpayers pay for your food shouldn’t be a big problem

Wall Street Apes

64,747 просмотров • 4 месяцев назад

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

elvis

32,623 просмотров • 1 месяц назад

If you own a tree business doing $2M to $5M in revenue, I want to pay for 30 minutes of your time. I’m 20 years old and on track to hit $1M in revenue with my land clearing business in 2026. I’ve started the process of bringing on a certified arborist to help me expand into residential tree work in 2027. I started climbing when I was 14 and worked for a few tree companies before I started clearing land. I'm so pumped to get back into tree work and have a ton of questions. DM or comment if you’re open to a Zoom. I’m happy to pay whatever rate you think is fair.

If you own a tree business doing $2M to $5M in revenue, I want to pay for 30 minutes of your time. I’m 20 years old and on track to hit $1M in revenue with my land clearing business in 2026. I’ve started the process of bringing on a certified arborist to help me expand into residential tree work in 2027. I started climbing when I was 14 and worked for a few tree companies before I started clearing land. I'm so pumped to get back into tree work and have a ton of questions. DM or comment if you’re open to a Zoom. I’m happy to pay whatever rate you think is fair.

Owen

47,110 просмотров • 3 месяцев назад

This has to be a left shoulder turn into a right hook, or if you're gonna spin back, it has to be a right shoulder left hook. With Jaren Jackson Jr. on him, there is not going to be an opportunity to to just turn and dunk or lay it in. So it's pretty simple to me: Until there is a signal of a post move being developed in these positions, Gobert shouldn't be allowed to duck-in on this big of a defender. JJJ is big and good at defense! If it's someone you can just turn around and dunk on, sure, feed it. But this isn't that -- like very obviously.

This has to be a left shoulder turn into a right hook, or if you're gonna spin back, it has to be a right shoulder left hook. With Jaren Jackson Jr. on him, there is not going to be an opportunity to to just turn and dunk or lay it in. So it's pretty simple to me: Until there is a signal of a post move being developed in these positions, Gobert shouldn't be allowed to duck-in on this big of a defender. JJJ is big and good at defense! If it's someone you can just turn around and dunk on, sure, feed it. But this isn't that -- like very obviously.

Dane Moore

41,490 просмотров • 1 год назад

I'm hoping that the damage to MCP's reputation gets undone this year Being able to dynamically get input from the user for subtool calls is incredible, and since it's a standard any agent that implements it gets to work with this

I'm hoping that the damage to MCP's reputation gets undone this year Being able to dynamically get input from the user for subtool calls is incredible, and since it's a standard any agent that implements it gets to work with this

Rhys

23,796 просмотров • 5 месяцев назад

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

elvis

37,016 просмотров • 2 месяцев назад

I’ve been MIA lately cause I was in a car accident, my car is totaled and some minor injuries. I’m currently not able to work for a bit so I started a gofundme to help during this time and get a new car. If you want to donate or share the link is below 🫶🏽

I’ve been MIA lately cause I was in a car accident, my car is totaled and some minor injuries. I’m currently not able to work for a bit so I started a gofundme to help during this time and get a new car. If you want to donate or share the link is below 🫶🏽

Bruja Baddie 🔮

25,916 просмотров • 1 год назад

If it was a man doing this to a little girl, everyone (including men) on Twitter would be calling for his arrest. Women get away with a lot of stuff in this country. A LOT.

If it was a man doing this to a little girl, everyone (including men) on Twitter would be calling for his arrest. Women get away with a lot of stuff in this country. A LOT.

Aku N’esi Obi Ike (I)

649,492 просмотров • 1 год назад

🐈: They said the company is openly watching the live? ...... You work at HYBE? If you work at HYBE, it's fine to watch, right? This is work, after all. If you work at HYBE lololol. Looks like you do work at HYBE! he even joked about it, give it a rest.

🐈: They said the company is openly watching the live? ...... You work at HYBE? If you work at HYBE, it's fine to watch, right? This is work, after all. If you work at HYBE lololol. Looks like you do work at HYBE! he even joked about it, give it a rest.

Sawon ◝◡◜

147,162 просмотров • 1 месяц назад

I love this new battery dock for Apple Vision Pro. It's a game changer if you use Mac Virtual Display. It gives the battery a dedicated home, makes it really easy to connect to power, and lets me jump between work and other visionOS experiences. Link below.

I love this new battery dock for Apple Vision Pro. It's a game changer if you use Mac Virtual Display. It gives the battery a dedicated home, makes it really easy to connect to power, and lets me jump between work and other visionOS experiences. Link below.

Justin Ryan

15,678 просмотров • 1 месяц назад

"I think when I didn't work out, there was a misconception that I didn't want to play for them for some reason... But definitely happy that Utah picked me and can't wait to get there and go work... I think it's a great fit." - Darryn Peterson Looks like DP is happy to come to Utah!

"I think when I didn't work out, there was a misconception that I didn't want to play for them for some reason... But definitely happy that Utah picked me and can't wait to get there and go work... I think it's a great fit." - Darryn Peterson Looks like DP is happy to come to Utah!

SleeperJazz

75,708 просмотров • 2 месяцев назад

Effort doesn't promise you success, but it creates opportunity. "You can do all the work. It's not a guarantee. Nothing is guaranteed. If you do nothing, you get nothing. If you do something, you have a chance to get something. The more you do, the more you have a chance."

Effort doesn't promise you success, but it creates opportunity. "You can do all the work. It's not a guarantee. Nothing is guaranteed. If you do nothing, you get nothing. If you do something, you have a chance to get something. The more you do, the more you have a chance."

The Winning Difference

37,981 просмотров • 1 год назад

Jayson Tatum on if he feels like he’s close and how realistic a return is: “I think it's just important that I've worked this hard to just kind of get myself in a position where it could be a conversation.”

Jayson Tatum on if he feels like he’s close and how realistic a return is: “I think it's just important that I've worked this hard to just kind of get myself in a position where it could be a conversation.”

Justin Turpin

32,437 просмотров • 6 месяцев назад

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

I just built my own wiki generator plugin for my agents. My agents can now generate wikis for anything I ask. One of my favorite wikis is called PaperWiki. This is a great example of what Andrej Karpathy describes. It uses obsidian vaults to organize papers, retrieve LLM-generated summaries, diagrams, and other advanced views for paper exploration. When Obsidian UI is not enough, I use my own artifact generator inside my agent orchestrator (see clip for example). This allows my agents to build any kind of view or exploration feature that I need. The papers are all curated with automations and several rules/patterns I have manually built over the years. On the surface, this looks basic. But behind the scenes, there are advanced search capabilities, connections, metadata, derived data, and other interesting bits of information that are extremely useful for my research agents. This is mostly built for agents. The artifact preview is just a high-level way to validate and quickly assess the quality of the wiki, suggest improvements, and it's also great for research. I use tobi lutke's qmd for all search capabilities. Everything is markdown. The summaries and even the diagrams. The wiki updates on its own based on several automations I have optimized over the past couple of weeks. The wiki grows and self-improves based on several requirements important for my research use cases. This is as personalized as it gets. There is nothing like it out there. And I use my research expertise to continue improving it over time. This is a vanilla wiki. There are so many things I want to build on top of this. Different aggregations, views, artifacts, etc. All to help automate more of my research work and accelerate productivity. I think the biggest leverage here is how powerful this could be for discovery and experimentation. One of my goals is to use it to find deeper connections and insights that would otherwise elude the top human researchers and use those to generate interesting new hypotheses and research experiments. That way, my agents can use autoresearch to explore research ideas at the frontier. Stay tuned for more.

elvis

67,153 просмотров • 4 месяцев назад

Miguel Angel Jiménez has one of the smoothest, most unhurried, and most effective golf swings ever put on display. Never a care in the world. He simply swings from the inside, lets it work over the top, and then strolls away from aces like he's competing in a Macarena contest. The beauty of Jiménez's motion isn't just that it works—it's that it's built to last. If you'd like to learn the classic over-the-top swing that's stood the test of time, check out my Classic OTT instructional video. Learn to swing with rhythm, freedom, and a move you can trust for a lifetime. For lessons or information, email: christo@classicswing.golf #GolfSwing #MiguelAngelJimenez #ClassicSwing #GolfInstruction #OverTheTopSwing #GolfTips

Miguel Angel Jiménez has one of the smoothest, most unhurried, and most effective golf swings ever put on display. Never a care in the world. He simply swings from the inside, lets it work over the top, and then strolls away from aces like he's competing in a Macarena contest. The beauty of Jiménez's motion isn't just that it works—it's that it's built to last. If you'd like to learn the classic over-the-top swing that's stood the test of time, check out my Classic OTT instructional video. Learn to swing with rhythm, freedom, and a move you can trust for a lifetime. For lessons or information, email: [email protected] #GolfSwing #MiguelAngelJimenez #ClassicSwing #GolfInstruction #OverTheTopSwing #GolfTips

ChristoGarciaGolf

288,991 просмотров • 2 месяцев назад

I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :) I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p. But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them. But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?

I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :) I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p. But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them. But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?

Andrej Karpathy

1,650,521 просмотров • 5 месяцев назад

"It's just consistency. It's just good old fashioned 'do the work' every single day. Days that you don't feel like doing it, you show up and you do it. And when you do that -every day- for a long period of time, you wind up having a success that goes on for quite a few years."

"It's just consistency. It's just good old fashioned 'do the work' every single day. Days that you don't feel like doing it, you show up and you do it. And when you do that -every day- for a long period of time, you wind up having a success that goes on for quite a few years."

The Winning Difference

221,396 просмотров • 2 лет назад