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300 agents without shared context is just parallelized confusion. All 300 agents are useless if the research dies inside 300 separate documents. the real unlock is what happens after the search. in K3 graph-native setup: 300 agents 140 linked sources 100% traceable claims 9 contradictions surfaced and every new...

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Ricker

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15,323 次观看 • 1 个月前 •via X (Twitter)

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31 条评论

EagleVision 的头像
EagleVision1 个月前

that amount are useless if their results do not build one system

Ricker 的头像
Ricker1 个月前

nice point man, you right

karim 的头像
karim1 个月前

2,28 peix of dollar without iss gold

Morty 的头像
Morty1 个月前

This post is perfect addition to your article man

Ricker 的头像
Ricker1 个月前

thanks fam

Atenov int. 的头像
Atenov int.1 个月前

This is what I meant about active research actually needing structure. Watching one shared graph update beats reading 10 different summaries every time

Ricker 的头像
Ricker1 个月前

i need more people like you who understand it right

Apex 的头像
Apex1 个月前

Every source change the graph is main sentence

MopOzeu 的头像
MopOzeu1 个月前

Added it to my bookmark. thanks

morph 的头像
morph1 个月前

it's hard to build, but it worth as hell

Ricker 的头像
Ricker1 个月前

100% dude

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack1 个月前

@0xRicker totally agree. connecting those dots is where the magic happens. without it, it's just noise in a bunch of silos.

starmex 的头像
starmex1 个月前

bookmarked ricker, saving the whole graph-native research writeup for tonight fam

Ricker 的头像
Ricker1 个月前

let me know what you think after reading

VALIX 的头像
VALIX1 个月前

In my opinion, the hardest part here is making these agents work in sync and not repeat the same tasks multiple times, changing the final result

CryptoCraftsman 的头像
CryptoCraftsman1 个月前

Graphs over chaos, the only way 300 agents actually work together

Jurly 的头像
Jurly1 个月前

provenance and contradiction handling become the bottleneck long before agent count does.

Moysha 的头像
Moysha1 个月前

More agents without shared context won’t help

Carlos Johnson 的头像
Carlos Johnson1 个月前

Hmm, is this like, a *lot* of work for kinda not much payoff then

probe 的头像
probe1 个月前

The shared graph is the whole thing. We ran a swarm over a doc set and every summary looked fine until we tried to reconcile them. Contradictions surfaced is the number I'd actually watch.

CreateOS 的头像
CreateOS1 个月前

This is the real bottleneck in multi-agent systems: not orchestration, provenance. If you can't trace a claim back to its source, you don't have 300 agents working together, you have 300 agents you can't trust

Saman Ahmed 的头像
Saman Ahmed1 个月前

That’s where a graph starts looking much more useful than another batch of summaries.

SEO Mastery 的头像
SEO Mastery1 个月前

Shared context is the actual hard part

Damir Akaza 的头像
Damir Akaza1 个月前

Oh wow, that’s cool, now I know a little more, thanks.

RemoteBrowser 的头像
RemoteBrowser1 个月前

The real bottleneck is usually the handoff, not the search. We found that forcing agents to write structured claims into a shared graph beats hoping they'll read each other's docs. Traceability is the killer feature.

AI Mastery Guide 的头像
AI Mastery Guide1 个月前

Shared graph over isolated summaries, makes sense

Genius💡💹🧲 🤖 的头像
Genius💡💹🧲 🤖1 个月前

Traceable claims make massive agent swarms actually useful for research.

Dom_Parser 的头像
Dom_Parser1 个月前

Another day, another fake node-graph dashboard farming impressions with buzzwords. While they're LARPing as AI researchers, real engineers are shipping bare-metal endpoint defense. Local tun0 interception. Zero cloud telemetry. Real code. 🛡️💻

rewind 的头像
rewind1 个月前

shared context is everything

Piro Arroyo 的头像
Piro Arroyo1 个月前

Yep. Mostly useless

Akbar Shaik 的头像
Akbar Shaik1 个月前

This is the difference between parallelism and collaboration. 300 agents working independently is parallelism. 300 agents contributing evidence to a shared structure, seeing relevant prior findings, and triggering contradiction checks is collaboration.

相关视频

Found a setup that replaces a team of researchers with 300 AI agents and one shared graph. the interesting part is what happens after all 300 come back with different pieces of the answer. because now you don’t have 300 agents. you have: 300 outputs → hundreds of sources → repeated entities → overlapping claims → contradictions → thousands of possible relationships and all of that has to collapse into one structure. that’s what the context graph is doing. instead of: agent → report you get: agents → sources → entities → edges → contradictions → verification → one graph every source becomes a node. every shared entity creates a connection. 300 agents can create information. the graph is what turns that information into memory the next agent can actually use. that’s the anatomy behind the setup ↓
0:33

Found a setup that replaces a team of researchers with 300 AI agents and one shared graph. the interesting part is what happens after all 300 come back with different pieces of the answer. because now you don’t have 300 agents. you have: 300 outputs → hundreds of sources → repeated entities → overlapping claims → contradictions → thousands of possible relationships and all of that has to collapse into one structure. that’s what the context graph is doing. instead of: agent → report you get: agents → sources → entities → edges → contradictions → verification → one graph every source becomes a node. every shared entity creates a connection. 300 agents can create information. the graph is what turns that information into memory the next agent can actually use. that’s the anatomy behind the setup ↓

Ricker

19,999 次观看 • 1 个月前

300 agents sounds powerful until you realize they can create 44,850 possible agent-to-agent relationships. that’s the real scaling problem. 1 agent searches 10 pages. 10 agents can search 100. 300 agents can search thousands in parallel. but more agents also means more: • duplicate findings • shared sources • hidden relationships and if every agent returns its own isolated report, you’re left with 300 piles to reconcile manually. that’s why the graph matters. instead of: 300 agents → 300 reports you get: 300 agents → shared entities → edges → clusters → one connected research map 100 nodes already create up to 4,950 pairwise relationships. more agents give you more information. the graph is what stops that information from becoming noise. full breakdown in the article below ↓
0:40

300 agents sounds powerful until you realize they can create 44,850 possible agent-to-agent relationships. that’s the real scaling problem. 1 agent searches 10 pages. 10 agents can search 100. 300 agents can search thousands in parallel. but more agents also means more: • duplicate findings • shared sources • hidden relationships and if every agent returns its own isolated report, you’re left with 300 piles to reconcile manually. that’s why the graph matters. instead of: 300 agents → 300 reports you get: 300 agents → shared entities → edges → clusters → one connected research map 100 nodes already create up to 4,950 pairwise relationships. more agents give you more information. the graph is what stops that information from becoming noise. full breakdown in the article below ↓

Ricker

26,875 次观看 • 1 个月前

300 agents can find 1,000 sources. that still doesn’t make the research good the interesting part starts when those sources are forced to check each other. in K3 graph-native setup: 300 agents → 140 linked sources → 100% traceable claims → 9 contradictions surfaced one source confirms a number. another challenges it. a third connects the same entity from a different angle. so instead of: source → summary you get: source → entity → overlap → contradiction → verification → graph every new source becomes a node. if 3–4 independent sources support the same claim, confidence goes up. if 2 sources disagree, the contradiction gets flagged. if a claim has only 1 supporting source, it stays weak. a normal research workflow could have quietly shipped those 9 contradictions as facts. that’s why the breakthrough isn’t just 300 agents running in parallel. full breakdown in the article below.
0:39

300 agents can find 1,000 sources. that still doesn’t make the research good the interesting part starts when those sources are forced to check each other. in K3 graph-native setup: 300 agents → 140 linked sources → 100% traceable claims → 9 contradictions surfaced one source confirms a number. another challenges it. a third connects the same entity from a different angle. so instead of: source → summary you get: source → entity → overlap → contradiction → verification → graph every new source becomes a node. if 3–4 independent sources support the same claim, confidence goes up. if 2 sources disagree, the contradiction gets flagged. if a claim has only 1 supporting source, it stays weak. a normal research workflow could have quietly shipped those 9 contradictions as facts. that’s why the breakthrough isn’t just 300 agents running in parallel. full breakdown in the article below.

Ricker

227,257 次观看 • 1 个月前

300 agents just worked in parallel. the hard part starts when they’re done. The real unlock is the shared context graph. 100 entities already have 4,950 possible pairwise relationships. at 300, that jumps to 44,850. 300 agents → thousands of steps → shared entities → shared relationships → one connected memory layer Every run adds more structure instead of starting from zero. That means the next agent doesn’t just get more data. It gets a better map of how everything connects. That’s the part people are underestimating about multi-agent systems.
0:40

300 agents just worked in parallel. the hard part starts when they’re done. The real unlock is the shared context graph. 100 entities already have 4,950 possible pairwise relationships. at 300, that jumps to 44,850. 300 agents → thousands of steps → shared entities → shared relationships → one connected memory layer Every run adds more structure instead of starting from zero. That means the next agent doesn’t just get more data. It gets a better map of how everything connects. That’s the part people are underestimating about multi-agent systems.

Ricker

34,737 次观看 • 1 个月前

300 agents can still give you bad research. unless the sources can check each other. the interesting part is what happens when every source can see every other source. K3 swarm doesn't just dump research into 300 separate write-ups. • every source becomes a node. • shared entities become edges. • single-source claims stick out as weak. so instead of: `300 agents → 300 answers` you get: `300 agents → sources → links → one research graph` and that's where things start showing up that no individual agent was explicitly asked to find. the article's example ends with 140 linked sources, every claim traceable to its origin, and 9 contradictions surfaced instead of silently shipped as facts. esearch stops being a pile of tabs. it becomes a system where the sources check each other. Book this & read full breakdown below
0:40

300 agents can still give you bad research. unless the sources can check each other. the interesting part is what happens when every source can see every other source. K3 swarm doesn't just dump research into 300 separate write-ups. • every source becomes a node. • shared entities become edges. • single-source claims stick out as weak. so instead of: `300 agents → 300 answers` you get: `300 agents → sources → links → one research graph` and that's where things start showing up that no individual agent was explicitly asked to find. the article's example ends with 140 linked sources, every claim traceable to its origin, and 9 contradictions surfaced instead of silently shipped as facts. esearch stops being a pile of tabs. it becomes a system where the sources check each other. Book this & read full breakdown below

Ricker

32,618 次观看 • 1 个月前

300 agents without context engineering is just 300 ways to lose track of the answer. a serious agent system starts looking less like: 1 model → 1 prompt → 1 answer and more like: 1 planner → N parallel agents → verifier → merge → memory → human gate once you have 10, 50, or 300 agents, the number of possible relationships explodes. 100 entities = 4,950 possible pairwise relationships. 300 = 44,850. 1,000 = 499,500. that’s why “just give the model more context” stops working surprisingly fast. you need different agents seeing different slices of the graph. the architecture around the model is becoming the product. full article below ↓
0:40

300 agents without context engineering is just 300 ways to lose track of the answer. a serious agent system starts looking less like: 1 model → 1 prompt → 1 answer and more like: 1 planner → N parallel agents → verifier → merge → memory → human gate once you have 10, 50, or 300 agents, the number of possible relationships explodes. 100 entities = 4,950 possible pairwise relationships. 300 = 44,850. 1,000 = 499,500. that’s why “just give the model more context” stops working surprisingly fast. you need different agents seeing different slices of the graph. the architecture around the model is becoming the product. full article below ↓

Ricker

32,937 次观看 • 1 个月前

300 agents shouldn’t be running all day. they should just appear for 28 minutes, finish the job, and disappear until next task the bigger an agent swarm gets, the less the individual agent matters. what matters is the system connecting them. 100 agents can create up to 4,950 possible pairwise relationships. 300 = 44,850. when a real signal appears, it spins up the workforce: 1 trigger → 100–300 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that’s before adding: sources → claims → memory → tool calls → contradictions → retries so the architecture has to continuously compress all that activity into one shared state. more agents gives you more compute. the graph is what stops that compute from turning into noise.
0:40

300 agents shouldn’t be running all day. they should just appear for 28 minutes, finish the job, and disappear until next task the bigger an agent swarm gets, the less the individual agent matters. what matters is the system connecting them. 100 agents can create up to 4,950 possible pairwise relationships. 300 = 44,850. when a real signal appears, it spins up the workforce: 1 trigger → 100–300 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that’s before adding: sources → claims → memory → tool calls → contradictions → retries so the architecture has to continuously compress all that activity into one shared state. more agents gives you more compute. the graph is what stops that compute from turning into noise.

Ricker

76,968 次观看 • 1 个月前

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Now everyone is building agentic graphs" In a 20-minute session, an Anthropic team member breaks down how graph engineering turns isolated agents into systems that improve themselves The real setup is Claude running through graph workflows, plan mode, and dynamic orchestration Agents → Loops → Graphs → Self-Improving Systems Better than most $300 graph engineering courses Bookmark and watch the talk Then read the article below
19:21

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Now everyone is building agentic graphs" In a 20-minute session, an Anthropic team member breaks down how graph engineering turns isolated agents into systems that improve themselves The real setup is Claude running through graph workflows, plan mode, and dynamic orchestration Agents → Loops → Graphs → Self-Improving Systems Better than most $300 graph engineering courses Bookmark and watch the talk Then read the article below

rari

76,231 次观看 • 1 个月前

LOOP + GRAPH + CONTEXT ENGINEERING = THE KIMI K3 STACK THAT KEEPS RUNNING 300 Kimi K3 agents built one graph out of 100 companies. no 300 documents to read yourself kimi-swarm/ ├── swarm_launch.md task, sources, stop condition ├── CONSTRAINTS.md corrections from every past run ├── graph/ 384 entities, 1,064 typed edges, 8 closed verbs └── cron/ re-fires the swarm on schedule > one node sits at the center with 12 inbound edges - the vendor everyone depends on and nobody flagged in the prompt > 2 hops already answers most of what you'd actually ask it loop decides when it fires -> graph decides what's kept -> context decides what survives after the session closes how people are getting paid on this: > one build sold as a competitive teardown - replaces a four-figure analyst contract, done in an hour, unattended > the same graph on retainer - charge weekly for every new edge that shows up > wire the stack for someone else's market once it's proven on your own full version of this tree is in the article below
0:22

LOOP + GRAPH + CONTEXT ENGINEERING = THE KIMI K3 STACK THAT KEEPS RUNNING 300 Kimi K3 agents built one graph out of 100 companies. no 300 documents to read yourself kimi-swarm/ ├── swarm_launch.md task, sources, stop condition ├── CONSTRAINTS.md corrections from every past run ├── graph/ 384 entities, 1,064 typed edges, 8 closed verbs └── cron/ re-fires the swarm on schedule > one node sits at the center with 12 inbound edges - the vendor everyone depends on and nobody flagged in the prompt > 2 hops already answers most of what you'd actually ask it loop decides when it fires -> graph decides what's kept -> context decides what survives after the session closes how people are getting paid on this: > one build sold as a competitive teardown - replaces a four-figure analyst contract, done in an hour, unattended > the same graph on retainer - charge weekly for every new edge that shows up > wire the stack for someone else's market once it's proven on your own full version of this tree is in the article below

Mr. Buzzoni

15,462 次观看 • 1 个月前

Found a setup where 300 AI agents can wake up, finish the job, and disappear before you wake up. 300 agents that wake themselves up, divide the work, verify the result and shut down again is much more interesting. a self-running swarm should look closer to: schedule → planner → 100–300 agents → tools → memory → verifier → final output → sleep no one sitting there sending the next prompt. no babysitting. the system decides what needs to run, dispatches the swarm and keeps going until the job is finished. that’s the difference between using agents and actually building an agent system. that architecture makes way more sense to me than keeping 100–300 agents alive all day. full 7-step setup in the article below ↓
0:40

Found a setup where 300 AI agents can wake up, finish the job, and disappear before you wake up. 300 agents that wake themselves up, divide the work, verify the result and shut down again is much more interesting. a self-running swarm should look closer to: schedule → planner → 100–300 agents → tools → memory → verifier → final output → sleep no one sitting there sending the next prompt. no babysitting. the system decides what needs to run, dispatches the swarm and keeps going until the job is finished. that’s the difference between using agents and actually building an agent system. that architecture makes way more sense to me than keeping 100–300 agents alive all day. full 7-step setup in the article below ↓

Ricker

13,670 次观看 • 1 个月前

500 agents shouldn't be running all day. they should spin up for the exact window a task needs, finish it, and disappear until the next trigger. the bigger the swarm gets, the less any single agent matters. what matters is the system wiring them together. 100 agents can form up to 4,950 possible pairwise connections. 500 = 124,750. when a real signal lands, it spins up the whole workforce: 1 trigger → 100–500 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that's before you add: sources → claims → memory → tool calls → contradictions → retries so the architecture has to fold all of that activity into one shared state, continuously. more agents just buys you more raw compute. the graph is the only thing standing between 124,750 possible connections and pure noise.
0:20

500 agents shouldn't be running all day. they should spin up for the exact window a task needs, finish it, and disappear until the next trigger. the bigger the swarm gets, the less any single agent matters. what matters is the system wiring them together. 100 agents can form up to 4,950 possible pairwise connections. 500 = 124,750. when a real signal lands, it spins up the whole workforce: 1 trigger → 100–500 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that's before you add: sources → claims → memory → tool calls → contradictions → retries so the architecture has to fold all of that activity into one shared state, continuously. more agents just buys you more raw compute. the graph is the only thing standing between 124,750 possible connections and pure noise.

kocer

41,841 次观看 • 1 个月前

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
0:22

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 次观看 • 4 个月前

Andrew Ng just released a 1-hour course on what comes after RAG. How to turn raw documents into agent memory: 00:00 - Why larger context windows are not enough 02:18 - Extract entities, relationships, and sources 14:31 - Coordinate specialized agents inside one graph 30:43 - Build it with Google ADK and Neo4j 47:25 - Turn knowledge graphs into persistent memory Most people are still learning RAG. Andrew Ng is already teaching the layer that comes after it. Documents → Entities → Relationships → Agents → Knowledge Graph More context is the old workflow. Persistent graphs are the new memory layer. This 1-hour watch is worth more than most paid GraphRAG courses. Bookmark and watch it today Then read the full graph engineering roadmap below
1:00:53

Andrew Ng just released a 1-hour course on what comes after RAG. How to turn raw documents into agent memory: 00:00 - Why larger context windows are not enough 02:18 - Extract entities, relationships, and sources 14:31 - Coordinate specialized agents inside one graph 30:43 - Build it with Google ADK and Neo4j 47:25 - Turn knowledge graphs into persistent memory Most people are still learning RAG. Andrew Ng is already teaching the layer that comes after it. Documents → Entities → Relationships → Agents → Knowledge Graph More context is the old workflow. Persistent graphs are the new memory layer. This 1-hour watch is worth more than most paid GraphRAG courses. Bookmark and watch it today Then read the full graph engineering roadmap below

Lunar

42,118 次观看 • 2 个月前

Anthropic engineer: "At Anthropic, all of our engineers are running hundreds of agents The way you do it is graph engineering" In just 36 minutes, he shows how Anthropic runs hundreds of agents at once and what actually makes it possible Prompts → Agents → Loops → Graphs The part most people skip is the return path, where every accepted result becomes a constraint for the next run Same model, same tokens, completely different system Prompting is the old workflow Graph engineering is the next one Watch it today Then save the full graph engineering guide below before everyone starts catching up ↓
35:57

Anthropic engineer: "At Anthropic, all of our engineers are running hundreds of agents The way you do it is graph engineering" In just 36 minutes, he shows how Anthropic runs hundreds of agents at once and what actually makes it possible Prompts → Agents → Loops → Graphs The part most people skip is the return path, where every accepted result becomes a constraint for the next run Same model, same tokens, completely different system Prompting is the old workflow Graph engineering is the next one Watch it today Then save the full graph engineering guide below before everyone starts catching up ↓

Lunar

32,902 次观看 • 1 个月前

Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓
1:25:29

Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓

codila

378,966 次观看 • 1 个月前

WHOEVER BUILT THIS GROK BOT TRADING SWARM KNEW EXACTLY WHAT THEY WERE DOING 300 AGENTS DON’T NEED TO RUN 24/7. They just need to show up, analyze the market, finish the job, and disappear. Grok bot makes the decision. Kimi spins up the swarm. Then the agents attack the market from different angles: 300 agents → market data + news + on-chain signals → 140+ connected sources → contradictions surfaced → one consolidated trading signal One agent finds a setup. Another challenges it. A third checks the underlying data. Another runs the risk analysis. The system keeps the conflicting signals instead of blindly trusting the first answer. Then everything gets merged back into one trading decision. That’s the part people miss. The 300 agents aren’t the breakthrough. The coordination is. This is what a Grok powered AI trading desk starts looking like. FULL GUIDE ON HOW TO SET IT UP BELOW
0:40

WHOEVER BUILT THIS GROK BOT TRADING SWARM KNEW EXACTLY WHAT THEY WERE DOING 300 AGENTS DON’T NEED TO RUN 24/7. They just need to show up, analyze the market, finish the job, and disappear. Grok bot makes the decision. Kimi spins up the swarm. Then the agents attack the market from different angles: 300 agents → market data + news + on-chain signals → 140+ connected sources → contradictions surfaced → one consolidated trading signal One agent finds a setup. Another challenges it. A third checks the underlying data. Another runs the risk analysis. The system keeps the conflicting signals instead of blindly trusting the first answer. Then everything gets merged back into one trading decision. That’s the part people miss. The 300 agents aren’t the breakthrough. The coordination is. This is what a Grok powered AI trading desk starts looking like. FULL GUIDE ON HOW TO SET IT UP BELOW

MAX

38,807 次观看 • 1 个月前

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG
0:55

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

QuantHood

16,376 次观看 • 3 个月前

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents Close the loop and give the model a way to verify its own output" In a 20-minute session, an Anthropic team member walks through how to build agents that improve after every run Agents → Harness → Loops → Self-Improvement the real setup is Claude running through systems that check, correct, and upgrade themselves the part most agent courses skip this is better than most $1,300 agent engineering courses bookmark and watch the talk then read the full article below
19:45

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents Close the loop and give the model a way to verify its own output" In a 20-minute session, an Anthropic team member walks through how to build agents that improve after every run Agents → Harness → Loops → Self-Improvement the real setup is Claude running through systems that check, correct, and upgrade themselves the part most agent courses skip this is better than most $1,300 agent engineering courses bookmark and watch the talk then read the full article below

Callan

14,956 次观看 • 10 天前

This K3 context graph playbook is f*cking gold A senior data engineer just dropped the full 8-step method for building agent graphs that keep source disagreements instead of averaging them into fake certainty, I compiled it into a walkthrough: the shift: instead of asking one summarizer to reconcile five feeds, they build a graph that keeps every claim, marks every contradiction, and lets you query which conclusions actually have backup why flat reports fail: → 3 feeds report EV battery capacity growth: 41%, 34%, 19% → summarizer says "about a third" → nobody reported "about a third", the useful finding (19% counted passenger cars only) got averaged into a decimal → you never find out what got thrown away here's the 8-step K3 playbook: step 1 → name your sources by feed, every claim gets tagged with the feed it came from, unnamed sources produce untraceable claims step 2 → one node = one real entity, match on ticker or registration number so "Tesla", "Tesla Inc" and "TSLA" don't become three separate nodes with zero detected conflicts step 3 → lock unit, scope, and period for every field before launch, skip this and you get 40 fake conflicts that all say "your sources counted different things" step 4 → write CONNECT and CONFLICT rules in the launch prompt, CONFLICT tells K3 to keep both claims and tag the edge with reason (value, date, or scope), never average, never pick a winner step 5 → read the conflict count before the summary, zero conflicts across five feeds is a red flag, usually means all your agents hit the same aggregator wearing five hats step 6 → sort contradictions by reason, scope mismatch (fix definitions, re-run one field), stale data (keep newest, log old), real spread (keep both, this is your verify-by-hand shortlist) step 7 → run the drop-a-source test, ask which conclusions survive without each feed, single-source nodes are the ones that will embarrass you step 8 → extend the graph, keep open conflicts open, next run lands new claims on existing nodes instead of starting over the result: 100 nodes, 340 claims, 47 contradiction edges, 7 real disagreements worth an afternoon of verification, instead of one confident paragraph hiding all of it Confidence costs nothing to generate. It reads the same whether four sources agree or one unchecked feed said it once -- the contradiction edges are what let you tell them apart Read this playbook before running another agent swarm -- it breaks down the one architectural decision most orchestration tutorials skip Bookmark and build your own contradiction-aware graph from the full article below ↓
0:27

This K3 context graph playbook is f*cking gold A senior data engineer just dropped the full 8-step method for building agent graphs that keep source disagreements instead of averaging them into fake certainty, I compiled it into a walkthrough: the shift: instead of asking one summarizer to reconcile five feeds, they build a graph that keeps every claim, marks every contradiction, and lets you query which conclusions actually have backup why flat reports fail: → 3 feeds report EV battery capacity growth: 41%, 34%, 19% → summarizer says "about a third" → nobody reported "about a third", the useful finding (19% counted passenger cars only) got averaged into a decimal → you never find out what got thrown away here's the 8-step K3 playbook: step 1 → name your sources by feed, every claim gets tagged with the feed it came from, unnamed sources produce untraceable claims step 2 → one node = one real entity, match on ticker or registration number so "Tesla", "Tesla Inc" and "TSLA" don't become three separate nodes with zero detected conflicts step 3 → lock unit, scope, and period for every field before launch, skip this and you get 40 fake conflicts that all say "your sources counted different things" step 4 → write CONNECT and CONFLICT rules in the launch prompt, CONFLICT tells K3 to keep both claims and tag the edge with reason (value, date, or scope), never average, never pick a winner step 5 → read the conflict count before the summary, zero conflicts across five feeds is a red flag, usually means all your agents hit the same aggregator wearing five hats step 6 → sort contradictions by reason, scope mismatch (fix definitions, re-run one field), stale data (keep newest, log old), real spread (keep both, this is your verify-by-hand shortlist) step 7 → run the drop-a-source test, ask which conclusions survive without each feed, single-source nodes are the ones that will embarrass you step 8 → extend the graph, keep open conflicts open, next run lands new claims on existing nodes instead of starting over the result: 100 nodes, 340 claims, 47 contradiction edges, 7 real disagreements worth an afternoon of verification, instead of one confident paragraph hiding all of it Confidence costs nothing to generate. It reads the same whether four sources agree or one unchecked feed said it once -- the contradiction edges are what let you tell them apart Read this playbook before running another agent swarm -- it breaks down the one architectural decision most orchestration tutorials skip Bookmark and build your own contradiction-aware graph from the full article below ↓

Ridark

12,686 次观看 • 1 个月前

SpaceXAI just dropped full guide on how to run multiple agents "I stopped running Grok Bot as separate chats. now every project gets its own AI team, manager and task board" one Projects Manager agent creates the project, opens the channel and assigns the right agents: • coder • researcher • writer every team gets its own context, tasks and workspace → instead of managing prompts, you manage teams → instead of running one agent, you run an organization SpaceXAI shared the full setup, roles and workflow for running multiple GrokBot teams at once worth more than 100 multi-agent posts on X save now, then read how to build a parallel agent system in the article below
0:32

SpaceXAI just dropped full guide on how to run multiple agents "I stopped running Grok Bot as separate chats. now every project gets its own AI team, manager and task board" one Projects Manager agent creates the project, opens the channel and assigns the right agents: • coder • researcher • writer every team gets its own context, tasks and workspace → instead of managing prompts, you manage teams → instead of running one agent, you run an organization SpaceXAI shared the full setup, roles and workflow for running multiple GrokBot teams at once worth more than 100 multi-agent posts on X save now, then read how to build a parallel agent system in the article below

0xRafy

12,230 次观看 • 1 个月前