Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

A good technical LLM interview question: Your RAG chatbot is working as expected locally. You deploy it behind a load balancer with 3 replicas. Users report that it forgets what they just asked, and answers get worse with each restart. Why did this happen? (answer below) A local setup...

akshay_pachaar's profile picture

Akshay 🚀

285,744 subscribers

30,296 görüntüleme • 3 gün önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

A good technical LLM interview question: Your LLM chatbot takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamai’s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!
0:18

A good technical LLM interview question: Your LLM chatbot takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamai’s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!

Avi Chawla

21,423 görüntüleme • 17 gün önce

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓
0:15

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

Argona

32,475 görüntüleme • 28 gün önce

SOMEONE BUILT A TERMINAL TORRENT CLIENT THAT SEARCHES EVERY TRUSTED SOURCE AT ONCE AND DOWNLOADS STRAIGHT TO DISK finding a torrent in 2026 is miserable fake download buttons, popups that spawn three more tabs, and half the results are dead with zero seeders. so he built the opposite of all that: > type one query and it hits a curated set of trackers all at once > results stream back tagged with source, size and seeders as fast as each site answers > arrow to the one you want, press d, and it lands on your drive > if one source is down it just skips it and keeps searching the rest > downloads run in the background and pick up where they left off if you quit mid transfer the whole thing is one command. npx and youre running, all you need is node, no browser, no setup, nothing leaves your machine except the request itself. its called torlink, open source and mit. this is the kind of clean terminal tool that does one annoying thing perfectly
0:16

SOMEONE BUILT A TERMINAL TORRENT CLIENT THAT SEARCHES EVERY TRUSTED SOURCE AT ONCE AND DOWNLOADS STRAIGHT TO DISK finding a torrent in 2026 is miserable fake download buttons, popups that spawn three more tabs, and half the results are dead with zero seeders. so he built the opposite of all that: > type one query and it hits a curated set of trackers all at once > results stream back tagged with source, size and seeders as fast as each site answers > arrow to the one you want, press d, and it lands on your drive > if one source is down it just skips it and keeps searching the rest > downloads run in the background and pick up where they left off if you quit mid transfer the whole thing is one command. npx and youre running, all you need is node, no browser, no setup, nothing leaves your machine except the request itself. its called torlink, open source and mit. this is the kind of clean terminal tool that does one annoying thing perfectly

Om Patel

361,539 görüntüleme • 2 ay önce

Your body fat is a tank with a tap on it, and the tap narrows as the tank empties. - Plenty of fat: your body covers almost any deficit you ask it to - Getting leaner: it covers less each day - Properly lean: it covers very little, and the rest comes off the plate Which is why the first stone comes off while you barely notice, and the last one takes everything you have. So when you are lean, the shortfall has to arrive as food. And the industry hands you the wrong bag. Push the protein. Two hundred grams. Two fifty. Chicken breast on a kitchen scale. Past what you build with, protein is a fuel your liver has to strip the nitrogen off before you can use a calorie of it, and that machinery has a speed limit. So the extra hundred grams builds nothing and lifts nothing. Protein is what you build with. Fat and carbohydrate are what you lift with. Cut them both to make room for another shake and you have removed the thing that was moving the weight. Fat wins that choice. Twice the energy per gram. Leaves insulin alone, so the fat you are burning is not told to stay put every three hours. Keeps you full. And it is the same fuel your own stores are already releasing. Eat the fat. It is the one thing on the plate that matters more the leaner you get, and the first thing they told you to cut.
0:17

Your body fat is a tank with a tap on it, and the tap narrows as the tank empties. - Plenty of fat: your body covers almost any deficit you ask it to - Getting leaner: it covers less each day - Properly lean: it covers very little, and the rest comes off the plate Which is why the first stone comes off while you barely notice, and the last one takes everything you have. So when you are lean, the shortfall has to arrive as food. And the industry hands you the wrong bag. Push the protein. Two hundred grams. Two fifty. Chicken breast on a kitchen scale. Past what you build with, protein is a fuel your liver has to strip the nitrogen off before you can use a calorie of it, and that machinery has a speed limit. So the extra hundred grams builds nothing and lifts nothing. Protein is what you build with. Fat and carbohydrate are what you lift with. Cut them both to make room for another shake and you have removed the thing that was moving the weight. Fat wins that choice. Twice the energy per gram. Leaves insulin alone, so the fat you are burning is not told to stay put every three hours. Keeps you full. And it is the same fuel your own stores are already releasing. Eat the fat. It is the one thing on the plate that matters more the leaner you get, and the first thing they told you to cut.

Sama Hoole

23,527 görüntüleme • 19 gün önce

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.
0:16

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.

Hasan Toor

170,518 görüntüleme • 3 gün 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 ↓
0:15

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 • 16 gün önce

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.
0:19

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,932 görüntüleme • 9 ay önce

The new Product Page Header image in the App Store will not matter. First you need users to actually want to enter your App Store listing. And the reality is that a lot of users NEVER enter the listing. They search for something, see the app in the search results and download it directly from there. So the important update here is the new Search Results image. It's an app banner inside the App Store search results. And you should think about it as a persuasive ad. COPYWRITING will be what matters. Not the fancy "branding". Users are searching because they want an answer to something. Your creative needs to give them that answer immediately. And this becomes even more important with Apple Ads. You can pay to appear for the keyword, but your creative still needs to convince the user that your app is the answer they were searching for.
0:11

The new Product Page Header image in the App Store will not matter. First you need users to actually want to enter your App Store listing. And the reality is that a lot of users NEVER enter the listing. They search for something, see the app in the search results and download it directly from there. So the important update here is the new Search Results image. It's an app banner inside the App Store search results. And you should think about it as a persuasive ad. COPYWRITING will be what matters. Not the fancy "branding". Users are searching because they want an answer to something. Your creative needs to give them that answer immediately. And this becomes even more important with Apple Ads. You can pay to appear for the keyword, but your creative still needs to convince the user that your app is the answer they were searching for.

Teodora @DesignerAnts

69,462 görüntüleme • 28 gün önce

The Sabotaging Practice of Over Supply and Sameness in the NFT Space. The current zeitgeist of the NFT space is that the same artists are doing the same kind of work five times a year, with project after project leaving a trail of disappointment and discontent among collectors and all of us watching in disbelief as huge resources are extracted from the space over work that feels like it could be left as an "artist study." I understand that you can do what you want with your money as collectors, but we are killing the whole space with this incestuous practice. No artist is that prolific to be able to do 5 collections of 100+ pieces each every year and actually deliver innovation and some kind of creative evolution. Of course, they can pretend play that the work has something new, but there is no precedent nor proof that that has ever happened in the speed that it happens in the NFT space. Again, people are free to through away their resources on whatever they want but with this way of doing things, we more and more are going to start seeing the consequences. Oh! There are consequences? Yes. Maybe unintended, but there are. Let's see. Let's start with the loss of belief in the NFT space as somewhere where emerging artists can come and find support for their experiments. Why even bother to bring experiments, innovation, and new ways to think of art on the blockchain if the same people have all the collectors hypnotized with their magical flutes? Why even try to come to a space where taking risks and challenging the status quo (the mission of art!!!) is overlooked? This makes the NFT space a social club and not a space for art. I guess it is fine, but IMO it is a recipe for disaster. New collectors stay away because the art will slowly but surely become stale and un-challenging. Why even bother to come and see what is happening here if you can't, as a collector, see new weird and up-and-coming artists? The amount of noise emitted by the same artists doing the same art over and over, drowns out any new voices. Again. A recipe for disaster. The NFT space is becoming a space of disappointment and doubt. We think that collections going to zero one after the other, over and over, is not damaging? I feel we are kidding ourselves. Disappointment piles up, and again, the people who will hurt are the emerging artists, the new blood, the ones who are willing to risk the most and, in return, put fire in this cold space of sameness. I love this space—don't get me wrong—it has changed my life, and I believe it has a ton of potential, but things need to change for it to become a beacon of light in art. But we need to support new voices. We need to support new ideas. The challenge is huge. I hope to contribute all I can to this change. I hope more and more see how exciting it is to go out and try to discover what else is out there and move this space forward. But again, I understand the leaps of faith needed, but if there is a space that is based on that, it's the NFT space...so there is hope. We will see. 📺by Boldtron
0:12

The Sabotaging Practice of Over Supply and Sameness in the NFT Space. The current zeitgeist of the NFT space is that the same artists are doing the same kind of work five times a year, with project after project leaving a trail of disappointment and discontent among collectors and all of us watching in disbelief as huge resources are extracted from the space over work that feels like it could be left as an "artist study." I understand that you can do what you want with your money as collectors, but we are killing the whole space with this incestuous practice. No artist is that prolific to be able to do 5 collections of 100+ pieces each every year and actually deliver innovation and some kind of creative evolution. Of course, they can pretend play that the work has something new, but there is no precedent nor proof that that has ever happened in the speed that it happens in the NFT space. Again, people are free to through away their resources on whatever they want but with this way of doing things, we more and more are going to start seeing the consequences. Oh! There are consequences? Yes. Maybe unintended, but there are. Let's see. Let's start with the loss of belief in the NFT space as somewhere where emerging artists can come and find support for their experiments. Why even bother to bring experiments, innovation, and new ways to think of art on the blockchain if the same people have all the collectors hypnotized with their magical flutes? Why even try to come to a space where taking risks and challenging the status quo (the mission of art!!!) is overlooked? This makes the NFT space a social club and not a space for art. I guess it is fine, but IMO it is a recipe for disaster. New collectors stay away because the art will slowly but surely become stale and un-challenging. Why even bother to come and see what is happening here if you can't, as a collector, see new weird and up-and-coming artists? The amount of noise emitted by the same artists doing the same art over and over, drowns out any new voices. Again. A recipe for disaster. The NFT space is becoming a space of disappointment and doubt. We think that collections going to zero one after the other, over and over, is not damaging? I feel we are kidding ourselves. Disappointment piles up, and again, the people who will hurt are the emerging artists, the new blood, the ones who are willing to risk the most and, in return, put fire in this cold space of sameness. I love this space—don't get me wrong—it has changed my life, and I believe it has a ton of potential, but things need to change for it to become a beacon of light in art. But we need to support new voices. We need to support new ideas. The challenge is huge. I hope to contribute all I can to this change. I hope more and more see how exciting it is to go out and try to discover what else is out there and move this space forward. But again, I understand the leaps of faith needed, but if there is a space that is based on that, it's the NFT space...so there is hope. We will see. 📺by Boldtron

alejandro cartagena

98,261 görüntüleme • 2 yıl önce

Two posts in one day calling for the end of John Cleese. One says he has allied himself with the darkest forces in Britain and has become the caricature he once mocked. The other, from a Muslim outlet, argues plainly that he should be de-platformed by respectable institutions. De-platformed. Respectable institutions. Say those words slowly and try to hear how they would have landed in 1972. The man co-wrote a film so blasphemous the BBC would not touch it, that was banned outright in Norway and by councils across England, and that two bishops went on television to denounce while he sat there and let them. Life of Brian survived the actual Church of England. It is now being reviewed by a newsletter. And the accusation is that he has become the caricature he once mocked, which is a remarkable sentence to write about a man whose entire body of work was aimed at exactly this. The Ministry of Silly Walks was a joke about a government department funding nonsense with a straight face. Now we have men in offices deciding which comedians remain permissible, and doing it with no jokes at all. At least the Ministry had the decency to be funny. Let me be fair, because I am always fair, and because being fair is what makes the rest of this land. Criticism is not censorship. Both of those men have every right to say Cleese is wrong, tasteless, or past it. That is speech. Nobody owes an 86 year old comedian applause. But there is a hard line between you are wrong and you should not be permitted to speak, and one of those posts crossed it in the headline. The first is an argument. The second is an eviction notice. And notice what the eviction is actually for. He said things about a religion. Not about a person. A religion, which is a set of ideas, and ideas do not have rights. People do. In England you can still say anything you like about the Church of England, and Cleese made a career of it, and nobody drafted a newsletter about respectable institutions. Here is what is wrong with Britain, and I say it with real affection because I am going there next month. A country that gave the world Magna Carta, the common law, and the funniest people who have ever lived has developed the habit of asking whether a joke is allowed. That question has an answer over here. John, if it gets any worse, come to America. Say every single thing you think. Say the wrong ones. Say the ones that would end you at home. Nobody will de-platform you and nobody will send a constable. Bring the silly walk. We have room. 🦋
0:15

Two posts in one day calling for the end of John Cleese. One says he has allied himself with the darkest forces in Britain and has become the caricature he once mocked. The other, from a Muslim outlet, argues plainly that he should be de-platformed by respectable institutions. De-platformed. Respectable institutions. Say those words slowly and try to hear how they would have landed in 1972. The man co-wrote a film so blasphemous the BBC would not touch it, that was banned outright in Norway and by councils across England, and that two bishops went on television to denounce while he sat there and let them. Life of Brian survived the actual Church of England. It is now being reviewed by a newsletter. And the accusation is that he has become the caricature he once mocked, which is a remarkable sentence to write about a man whose entire body of work was aimed at exactly this. The Ministry of Silly Walks was a joke about a government department funding nonsense with a straight face. Now we have men in offices deciding which comedians remain permissible, and doing it with no jokes at all. At least the Ministry had the decency to be funny. Let me be fair, because I am always fair, and because being fair is what makes the rest of this land. Criticism is not censorship. Both of those men have every right to say Cleese is wrong, tasteless, or past it. That is speech. Nobody owes an 86 year old comedian applause. But there is a hard line between you are wrong and you should not be permitted to speak, and one of those posts crossed it in the headline. The first is an argument. The second is an eviction notice. And notice what the eviction is actually for. He said things about a religion. Not about a person. A religion, which is a set of ideas, and ideas do not have rights. People do. In England you can still say anything you like about the Church of England, and Cleese made a career of it, and nobody drafted a newsletter about respectable institutions. Here is what is wrong with Britain, and I say it with real affection because I am going there next month. A country that gave the world Magna Carta, the common law, and the funniest people who have ever lived has developed the habit of asking whether a joke is allowed. That question has an answer over here. John, if it gets any worse, come to America. Say every single thing you think. Say the wrong ones. Say the ones that would end you at home. Nobody will de-platform you and nobody will send a constable. Bring the silly walk. We have room. 🦋

Selene Mariposa

269,846 görüntüleme • 24 gün önce

There is a room in Málaga that was built to be the closest thing on earth to standing inside heaven. It is called the camarín of the Virgin of Victory, and it is hidden at the top of a tower inside the Santuario de la Victoria. To reach it, you climb and the ascent is the entire point... The building you are climbing through was completed in 1700, and it was designed as a single argument made in stone. At the bottom lies a crypt: a black chamber crowded with white plaster skeletons, a meditation on death and the brevity of life. From there a staircase rises, and as you climb it the light grows stronger and the imagery changes from bones to saints. The architects of the time understood this ascent as the soul's own journey, the dark crypt as the stage of penitence, the staircase as the stage of spiritual progress, and the room at the very top as the final stage: the union of the soul with the divine. That room at the top is the camarín, and its dome is one of the most extraordinary interiors in Spain... Every surface is covered in white and gold plasterwork. There is no empty space anywhere. The Baroque called this horror vacui, the horror of the void: the conviction that a space meant to represent heaven should not contain a single bare patch of stone. Out of that plasterwork emerge angels, flowers, birds, and mirrors. The mirrors are not decoration alone. They catch the light pouring in through the windows of the drum and throw it around the chamber, so that the gold seems to move and the whole room appears to shimmer and breathe. This wonder was built by people who believed that if you wanted to show a human being what heaven might feel like, you did not describe it to them. You built a room, and you let them climb into it... -- -- -- If you enjoyed this, I write a weekly newsletter read by over 50,000 people who love rediscovering the beauty of the past. You can join us here: If you'd like to support my work, a paid subscription is what makes it possible.
0:13

There is a room in Málaga that was built to be the closest thing on earth to standing inside heaven. It is called the camarín of the Virgin of Victory, and it is hidden at the top of a tower inside the Santuario de la Victoria. To reach it, you climb and the ascent is the entire point... The building you are climbing through was completed in 1700, and it was designed as a single argument made in stone. At the bottom lies a crypt: a black chamber crowded with white plaster skeletons, a meditation on death and the brevity of life. From there a staircase rises, and as you climb it the light grows stronger and the imagery changes from bones to saints. The architects of the time understood this ascent as the soul's own journey, the dark crypt as the stage of penitence, the staircase as the stage of spiritual progress, and the room at the very top as the final stage: the union of the soul with the divine. That room at the top is the camarín, and its dome is one of the most extraordinary interiors in Spain... Every surface is covered in white and gold plasterwork. There is no empty space anywhere. The Baroque called this horror vacui, the horror of the void: the conviction that a space meant to represent heaven should not contain a single bare patch of stone. Out of that plasterwork emerge angels, flowers, birds, and mirrors. The mirrors are not decoration alone. They catch the light pouring in through the windows of the drum and throw it around the chamber, so that the gold seems to move and the whole room appears to shimmer and breathe. This wonder was built by people who believed that if you wanted to show a human being what heaven might feel like, you did not describe it to them. You built a room, and you let them climb into it... -- -- -- If you enjoyed this, I write a weekly newsletter read by over 50,000 people who love rediscovering the beauty of the past. You can join us here: If you'd like to support my work, a paid subscription is what makes it possible.

James Lucas

69,389 görüntüleme • 3 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)
0:19

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

12,820 görüntüleme • 3 ay önce

There’s been two papers released in the past couple months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.
0:20

There’s been two papers released in the past couple months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.

Victoria Slocum

15,333 görüntüleme • 1 yıl önce

Europe is quietly becoming what the United States once promised the world. More and more people are looking at their best years ahead and choosing a place where everyday life is designed to work. Where the future feels stable enough to plan for. Where safety is not a luxury product. Where you can build a good life without gambling your health, your family, or your dignity on one bad month. In much of Europe, the “dream” is not about becoming a billionaire. It is about becoming unafraid. It is the freedom of walking home at night without scanning every shadow. The comfort of knowing that if you get sick, you do not need to calculate whether you can afford to be treated. The relief of having a society that still believes children should carry backpacks, not trauma, and definitely not weapons. The calm of streets built for human beings, not just cars. The ability to take a holiday without feeling like you are committing career suicide. The basic decency of labor protections that assume you are a person first and a resource second. And then there is the part people underestimate until they live it: the texture of life. The cities are older and more beautiful than you expect. The distances are smaller. Weekends are real. Food is real. Public spaces are not just decorative, they are functional. Parks are full. Cafes are full. Trains take you somewhere, often across borders, without turning travel into a stress test. You can live in one country, work with another, and visit a third like it is normal because, in many places, it is. The European dream is also a quiet confidence in the social contract. That if you contribute, the system does not abandon you. That you can raise a family without feeling like you are one accident away from ruin. That “getting ahead” does not require burning out. That a good society is one where normal people can live normal lives and still feel proud of them. This is why more and more Americans are not just visiting Europe, but staying. Some come for studies and never leave. Some arrive for a job and realise the lifestyle is the real promotion. Some originally planned a one year experiment and then cannot imagine going back to a place where stress is treated as a personality trait and insecurity is marketed as freedom. Europe is not perfect. It has bureaucracy. It has politics. It has problems that deserve criticism. But in many European countries, life is still built around a simple idea: society should reduce fear, not monetise it. That is the new dream. And people can feel it the moment they arrive. If you could choose one thing to trade for a better life, what would it be: more income, or more security? And what do you think your country would have to change for people to stop leaving, and start staying? Stay connected, Follow Gandalv Gandalv
0:18

Europe is quietly becoming what the United States once promised the world. More and more people are looking at their best years ahead and choosing a place where everyday life is designed to work. Where the future feels stable enough to plan for. Where safety is not a luxury product. Where you can build a good life without gambling your health, your family, or your dignity on one bad month. In much of Europe, the “dream” is not about becoming a billionaire. It is about becoming unafraid. It is the freedom of walking home at night without scanning every shadow. The comfort of knowing that if you get sick, you do not need to calculate whether you can afford to be treated. The relief of having a society that still believes children should carry backpacks, not trauma, and definitely not weapons. The calm of streets built for human beings, not just cars. The ability to take a holiday without feeling like you are committing career suicide. The basic decency of labor protections that assume you are a person first and a resource second. And then there is the part people underestimate until they live it: the texture of life. The cities are older and more beautiful than you expect. The distances are smaller. Weekends are real. Food is real. Public spaces are not just decorative, they are functional. Parks are full. Cafes are full. Trains take you somewhere, often across borders, without turning travel into a stress test. You can live in one country, work with another, and visit a third like it is normal because, in many places, it is. The European dream is also a quiet confidence in the social contract. That if you contribute, the system does not abandon you. That you can raise a family without feeling like you are one accident away from ruin. That “getting ahead” does not require burning out. That a good society is one where normal people can live normal lives and still feel proud of them. This is why more and more Americans are not just visiting Europe, but staying. Some come for studies and never leave. Some arrive for a job and realise the lifestyle is the real promotion. Some originally planned a one year experiment and then cannot imagine going back to a place where stress is treated as a personality trait and insecurity is marketed as freedom. Europe is not perfect. It has bureaucracy. It has politics. It has problems that deserve criticism. But in many European countries, life is still built around a simple idea: society should reduce fear, not monetise it. That is the new dream. And people can feel it the moment they arrive. If you could choose one thing to trade for a better life, what would it be: more income, or more security? And what do you think your country would have to change for people to stop leaving, and start staying? Stay connected, Follow Gandalv Gandalv

Gandalv

989,075 görüntüleme • 6 ay önce

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable
0:18

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

Om Patel

176,154 görüntüleme • 2 ay önce

Every man lives two lives. The second begins when he realises that he can max out hypertrophy in 15 second sets. Four or five reps build muscle. The last four or five, where the bar slows on its own whether you like it or not. That is where the high-threshold motor units finally get called up. Where the cross-bridges are attached in maximum number at once. Where the fibre takes the tension it has been waiting all set for. Mechanical tension is the only language the muscle reads. It does not have a second one. Everything either side of that is filler, and somebody is paid to sell you filler. Tempo is filler. You are 30 to 50 per cent stronger lowering a weight than lifting it, so no descent on earth is slow enough to threaten you. Drag it out and all you have done is put a lighter weight on the half that counted. The pump is filler. Lactate infused straight into the blood grows nothing. Blood flow restricted on a resting muscle grows nothing. Swelling is a feeling and it gets no vote. The mind-muscle connection is filler. You cannot instruct a muscle in which fibres to use. You tell it to contract. The load decides the rest. So: a weight where the fourth rep slows by itself. Drive it. Stop one short. Rack it. Fifteen seconds. That set has already delivered everything your three sets of twelve spent forty minutes promising. Nobody has ever built a business on fifteen seconds of hard work, so they built one on everything you could bolt onto it.
0:16

Every man lives two lives. The second begins when he realises that he can max out hypertrophy in 15 second sets. Four or five reps build muscle. The last four or five, where the bar slows on its own whether you like it or not. That is where the high-threshold motor units finally get called up. Where the cross-bridges are attached in maximum number at once. Where the fibre takes the tension it has been waiting all set for. Mechanical tension is the only language the muscle reads. It does not have a second one. Everything either side of that is filler, and somebody is paid to sell you filler. Tempo is filler. You are 30 to 50 per cent stronger lowering a weight than lifting it, so no descent on earth is slow enough to threaten you. Drag it out and all you have done is put a lighter weight on the half that counted. The pump is filler. Lactate infused straight into the blood grows nothing. Blood flow restricted on a resting muscle grows nothing. Swelling is a feeling and it gets no vote. The mind-muscle connection is filler. You cannot instruct a muscle in which fibres to use. You tell it to contract. The load decides the rest. So: a weight where the fourth rep slows by itself. Drive it. Stop one short. Rack it. Fifteen seconds. That set has already delivered everything your three sets of twelve spent forty minutes promising. Nobody has ever built a business on fifteen seconds of hard work, so they built one on everything you could bolt onto it.

Sama Hoole

17,051 görüntüleme • 26 gün önce

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓
0:15

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓

Argona

723,300 görüntüleme • 26 gün önce

"PRICE IS WHAT YOU PAY. VALUE IS WHAT YOU GET." I keep buying $Kekec and I have a strong conviction. Here's Why: While the market is down, and Kekec is declining with it, there are data points that few are considering. Kekec borned in October and since then has been posting a different and original 30-second video every day, which I find extremely funny. For the past couple of months, they have also been posting daily on Instagram, and the attention on Kekec (which doesn't present itself on social media as a memecoin) is growing, moreover, it's increasing exponentially. The number of followers is increasing by about 500-1000 a day. This is largely due to the fact that they are not just focused on the main account but have several others that post reels and redirect to the main one. In short, an excellent strategy to keep growing more and more. Instagram link: Guess What? Not only are the followers increasing, but the team's workload is also growing. In fact, for a little over a month, they have also started pushing on YouTube, and the data here is promising as well. YouTube link: If we want to make a comparison, we can take Pudgy Penguins as an example, which has shown it can reach millions and millions of users without mentioning that they are a WEB3 company that owns an NFT collection. Or, if we want to be more appropriate by comparing one memecoin to another, we could take PONKE. Thanks to the use of social media and the quality of their content, they managed to achieve incredible numbers, which then translated into an increase in the coin's price. Kekec came before PONKE, but that doesn't necessarily mean it's better than PONKE. I believe PONKE is unbeatable in terms of content, but I want to make you reflect on an important point. PONKE came after KEKEC, and after PONKE's success, many coins have emerged trying to imitate it. One of KEKEC's strengths, in my opinion, is precisely the fact that it leverages social media without being a copy-paste. Instead, it is a unique meme derived from a 90's film, and it uses a unique form of content. In short, KEKEC > KEKEC and no one else. I want to conclude by suggesting you follow them on Instagram and evaluate not only the exponential growth of their followers day by day but also observe how the views of each reel increase accordingly. Pay special attention to the comments. Many of the people commenting have no idea what it is, and you can see from the comments how Kekec generates particular emotions in people—strange but still emotions. Personally, I believe that when something is unique and even very strange, it needs time to be adopted. However, once it happens, it usually explodes and spreads like never before. A few days ago, a Kekec video was posted by a very popular meme page. They probably don't know what Kekec is about but thought the video could spark interest among their followers. How many other pages will do the same? Lastly, but not least, I want to point out how Kekec maintains a good market cap despite everything that has happened in the crypto world since October 2023. As far as I know and have personally observed, everything is extremely organic. There is no cabal behind it, and the quality is not reflected in a single jpeg but in work that has been ongoing daily for months. Every day they work harder, and the quality of their videos grows as well. I have no affiliations with the team, but I believe that Kekec truly deserves more in this world where we push celebrity or cabal-backed coins to hundreds of millions in market cap. I keep buying because the numbers suggest so. Don't just evaluate the chart (price), evaluate the data (value). BÂLKÂN DWÂRF
0:15

"PRICE IS WHAT YOU PAY. VALUE IS WHAT YOU GET." I keep buying $Kekec and I have a strong conviction. Here's Why: While the market is down, and Kekec is declining with it, there are data points that few are considering. Kekec borned in October and since then has been posting a different and original 30-second video every day, which I find extremely funny. For the past couple of months, they have also been posting daily on Instagram, and the attention on Kekec (which doesn't present itself on social media as a memecoin) is growing, moreover, it's increasing exponentially. The number of followers is increasing by about 500-1000 a day. This is largely due to the fact that they are not just focused on the main account but have several others that post reels and redirect to the main one. In short, an excellent strategy to keep growing more and more. Instagram link: Guess What? Not only are the followers increasing, but the team's workload is also growing. In fact, for a little over a month, they have also started pushing on YouTube, and the data here is promising as well. YouTube link: If we want to make a comparison, we can take Pudgy Penguins as an example, which has shown it can reach millions and millions of users without mentioning that they are a WEB3 company that owns an NFT collection. Or, if we want to be more appropriate by comparing one memecoin to another, we could take PONKE. Thanks to the use of social media and the quality of their content, they managed to achieve incredible numbers, which then translated into an increase in the coin's price. Kekec came before PONKE, but that doesn't necessarily mean it's better than PONKE. I believe PONKE is unbeatable in terms of content, but I want to make you reflect on an important point. PONKE came after KEKEC, and after PONKE's success, many coins have emerged trying to imitate it. One of KEKEC's strengths, in my opinion, is precisely the fact that it leverages social media without being a copy-paste. Instead, it is a unique meme derived from a 90's film, and it uses a unique form of content. In short, KEKEC > KEKEC and no one else. I want to conclude by suggesting you follow them on Instagram and evaluate not only the exponential growth of their followers day by day but also observe how the views of each reel increase accordingly. Pay special attention to the comments. Many of the people commenting have no idea what it is, and you can see from the comments how Kekec generates particular emotions in people—strange but still emotions. Personally, I believe that when something is unique and even very strange, it needs time to be adopted. However, once it happens, it usually explodes and spreads like never before. A few days ago, a Kekec video was posted by a very popular meme page. They probably don't know what Kekec is about but thought the video could spark interest among their followers. How many other pages will do the same? Lastly, but not least, I want to point out how Kekec maintains a good market cap despite everything that has happened in the crypto world since October 2023. As far as I know and have personally observed, everything is extremely organic. There is no cabal behind it, and the quality is not reflected in a single jpeg but in work that has been ongoing daily for months. Every day they work harder, and the quality of their videos grows as well. I have no affiliations with the team, but I believe that Kekec truly deserves more in this world where we push celebrity or cabal-backed coins to hundreds of millions in market cap. I keep buying because the numbers suggest so. Don't just evaluate the chart (price), evaluate the data (value). BÂLKÂN DWÂRF

m0ment0

133,250 görüntüleme • 2 yıl önce

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots
0:17

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

494,735 görüntüleme • 18 gün önce