Kyver

Building agentic systems.
Software 3.0.
Sharing ideas on AI.
clarity, create, swift, wise

[Kyver @0xkyver]

@candlesclub @zerqfer True

[ZER @zerqfer]

https://t.co/OUtWwgPFXN

[Kyver @0xkyver]

I turned Grok into an 8-bit Overlord.

While most people are manually refreshing charts, my swarm of autonomous agents is playing a completely different game. They scan the mempool, snipe alpha, and collect profits like arcade coins.

THE ARCHITECTURE

Grok operates as the central command node.
Worker bots deploy on-chain to trace dev wallets in milliseconds.
The system instantly detects rugs and cuts exposure before the liquidity is pulled.
You are fighting player versus player in the trenches. My agents are playing a rigged single-player campaign.

Pure automation. Zero emotion.

[Kyver @0xkyver]

@polydao swapping rocket controller for quant alpha optimization is gonna print

[Kyver @0xkyver]

This dev turned his Grok trading bot into a live 3D spatial playbook.

He gamified his entire GMGN backend into an interactive virtual office.

HOW IT WORKS

Page 1 is the workforce.
40 Grok agents sprint across the grid to map out insider wallets and new mints in real-time.

Page 2 is the neural parser.
Agents sit at virtual terminals, auditing smart contracts and scraping Twitter sentiment in 12 milliseconds.

THE ADVANTAGE

Why stare at boring terminal logs when you can physically watch your AI army work in a glassmorphic 3D interface?

While normal traders stress over charts, this neural network filters the garbage and executes automatically.

The human sleeps. The 3D playbook prints SOL.

[Kyver @0xkyver]

@fluixoo @ilovemyfrenchie @Pumpfun shh dont publishd real alpha

[Kyver @0xkyver]

@fluixoo @ilovemyfrenchie @Pumpfun thx brother

[Kyver @0xkyver]

This dev @ilovemyfrenchie utilized 17 agents using grok and is KILLING memecoin trading on Pumpfun making 75 sol ($7,500) a day

THE BREAKDOWN:

1 scanner catches every new mint in real time
1 rugchecker validates authority + lp burn
1 dev profiler traces deployer wallets
1 social checker separates real hype from bot farms
2 snipers handle entries with separate slippage logic
2 ladders sell in tranches so exits don't dump the bag
1 risk manager caps size and kills on drawdown
3 wallet rotators spread entries across wallets
1 bookkeeper reconciles pnl every hour
1 postmortem bot retrains filters after every loss
1 monitor watches for rug signals post-entry
1 briefer builds a 30-second daily summary

the bots never touch his keys. they prepare. he signs.

the weirdest part? he's sharing the entire stack AND all alpha signals FOR FREE in his telegram group

no paid group. no course. no dm. just the repo and the channel.

link:https://t.co/0GNBYXpJQ0

[zostaff @zostaff]

https://t.co/5mIwoVLuaq

[Kyver @0xkyver]

I found the repo that solved the most annoying problem in AI engineering: connecting your agent to real tools without writing custom glue code for each one.

Composio gives your agent access to 1,000+ integrations out of the box. Slack, GitHub, Notion, Salesforce, databases — all through one standardized interface.

But here's the real value: it handles authentication and sandboxed execution. Your agent doesn't get raw API keys. It gets a secure, managed gateway. One MCP server. Zero "glue code."

30k stars. Native MCP support. Works with every major model and framework.

Stop spending 3 days integrating Slack into your agent. Spend 3 minutes. Bookmark this repo.

[Kyver @0xkyver]

I found the repo that solved the most annoying problem in AI engineering: connecting your agent to real tools without writing custom glue code for each one.

Composio gives your agent access to 1,000+ integrations out of the box. Slack, GitHub, Notion, Salesforce, databases — all through one standardized interface.

But here's the real value: it handles authentication and sandboxed execution. Your agent doesn't get raw API keys. It gets a secure, managed gateway. One MCP server. Zero "glue code."

30k stars. Native MCP support. Works with every major model and framework.

Stop spending 3 days integrating Slack into your agent. Spend 3 minutes. Bookmark this repo.

[Kyver @0xkyver]

I built an AI agent from scratch in 50 lines of Python. No LangChain. No CrewAI. No frameworks at all.

And it actually worked. It could reason, call tools, observe results, and decide when to stop.

This tutorial just showed me that every framework is just a wrapper around one simple loop: Think, Act, Observe, Repeat. Once you see the loop, you can't unsee it.

It covers the ReAct architecture, tool calling with JSON schemas, and memory — all in pure Python. No magic, no abstractions, no dependency hell.

If you've been afraid to build agents because the frameworks look overwhelming — start here. Build the loop first. Add the framework later.

Bookmark this.

[Kyver @0xkyver]

I built an AI agent from scratch in 50 lines of Python. No LangChain. No CrewAI. No frameworks at all.

And it actually worked. It could reason, call tools, observe results, and decide when to stop.

This tutorial just showed me that every framework is just a wrapper around one simple loop: Think, Act, Observe, Repeat. Once you see the loop, you can't unsee it.

It covers the ReAct architecture, tool calling with JSON schemas, and memory — all in pure Python. No magic, no abstractions, no dependency hell.

If you've been afraid to build agents because the frameworks look overwhelming — start here. Build the loop first. Add the framework later.

Bookmark this.

[Kyver @0xkyver]

Do you know why 90% of agent tutorials are useless? They teach you chains. Chains are dead.

Production agents need loops. They need to self-correct, retry failed tool calls, and pause for human approval. That's what LangGraph does — it replaces your fragile chain with a stateful graph.

This hands-on tutorial just dropped and it finally explains the mental model: State, Nodes, Edges. No fluff, no 4-hour course. Just the core architecture you need to build agents that actually survive in production.

If you're still using sequential chains, you're building a house of cards. Watch this and upgrade. Bookmark it.

[Kyver @0xkyver]

Do you know why 90% of agent tutorials are useless? They teach you chains. Chains are dead.

Production agents need loops. They need to self-correct, retry failed tool calls, and pause for human approval. That's what LangGraph does — it replaces your fragile chain with a stateful graph.

This hands-on tutorial just dropped and it finally explains the mental model: State, Nodes, Edges. No fluff, no 4-hour course. Just the core architecture you need to build agents that actually survive in production.

If you're still using sequential chains, you're building a house of cards. Watch this and upgrade. Bookmark it.

[Kyver @0xkyver]

GRAPHITI JUST KILLED TRADITIONAL RAG BY GIVING YOUR AGENTS A TEMPORALLY-AWARE KNOWLEDGE GRAPH

instead of dumping static text chunks into a vector database and hoping for the best, this repo builds a "living" memory that tracks how facts and relationships change over time

ingest data -> extract entities -> map relationships -> add timestamps -> query context -> agent acts

/extract-entities builds the nodes / map-relationships connects the dots / add-timestamps ensures the agent knows WHAT is true right NOW / query-context pulls exactly what’s needed

the framework natively combines semantic embeddings with graph traversal, meaning your agent doesn't just match keywords, it actually understands the structure of your data

this is the community fixing the biggest memory bottleneck in long-running ai workflows. bookmark this

[Kyver @0xkyver]

I HAVE A CONFESSION: Evaluating an AI Agent by only checking its final answer is like grading a math test without looking at the work.

If your agent gets the right answer, but it took 45 looping tool calls and burned $5 in tokens to get there — that's not a success. That's a production nightmare waiting to happen.

This masterclass on Agent Observability just exposed how senior engineers actually debug. They don't look at the output. They use "Trajectory Evaluation" to trace every single reasoning step and tool execution in real-time.

Stop guessing why your agent failed. Watch this and learn how to actually see inside the black box. Bookmark it.

[Kyver @0xkyver]

DEERFLOW JUST HIT #1 ON GITHUB BY SOLVING THE BIGGEST BOTTLENECK IN LONG-HORIZON AGENT WORKFLOWS

instead of writing brittle python scripts to manage your sub-agents, this repo gives you a "super agent harness" that handles orchestration, memory and sandboxing out of the box

receive goal -> spawn specialists -> sandboxed execution -> aggregate context -> verify output -> resolve

/spawn-specialists creates isolated workers / sandboxed-execution prevents rogue edits / context-aggregator merges state / verify-output ensures alignment

the harness maintains a continuous graph of all sub-agent decisions, while git-native logs carry the exact failure points between steps

this is the open-source community packaging enterprise-grade orchestration into a single repo you can deploy in minutes. bookmark this

[Kyver @0xkyver]

I have a confession: we spend 90% of our time optimizing prompts and 10% on the architecture. It should be the exact opposite.

Frank Coyle just exposed the reality of "The Agentic Harness." He breaks down why models don't fail in production—the harness around them fails.

He skips the toy demos and shows how to build robust, fault-tolerant sandboxes that catch agent errors before they cause a catastrophe.

Stop writing giant prompts. Start building harnesses. Watch the full masterclass below.

[Kyver @0xkyver]

@slash1sol @MiniMax_AI sending every finisjhed task to an isolated veriefier that never saw the original chat history is top tier architecture

[Kyver @0xkyver]

I have a confession: we spend 90% of our time optimizing prompts and 10% on the architecture. It should be the exact opposite.

Frank Coyle just exposed the reality of "The Agentic Harness." He breaks down why models don't fail in production—the harness around them fails.

He skips the toy demos and shows how to build robust, fault-tolerant sandboxes that catch agent errors before they cause a catastrophe.

Stop writing giant prompts. Start building harnesses. Watch the full masterclass below.