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THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task →...

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Gipp 🦅

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11,709 görüntüleme • 2 ay önce •via X (Twitter)

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ANTHROPIC ENGINEER JUST SHOWS EXACTLY WHAT GRAPH ENGINEERING LOOKS LIKE WHEN A TASK RUNS THROUGH IT most people arguing about graphs online have never actually watched one execute Task → Researcher → Planner → Writer + Code Agent → Reviewer → Deploy six nodes, one shared state, graph completes itself while you watch loop mode gets disabled the second the task splits into real specialties, one agent stops trying to do everything at once reviewer catches a failure, kicks it straight back to the exact node that broke - not a full restart, no lost context the graph is not a fancier loop - it is the org chart your agents were missing bookmark this and watch it run, then read the article below to see why the timeline just found what production systems already do
0:10

ANTHROPIC ENGINEER JUST SHOWS EXACTLY WHAT GRAPH ENGINEERING LOOKS LIKE WHEN A TASK RUNS THROUGH IT most people arguing about graphs online have never actually watched one execute Task → Researcher → Planner → Writer + Code Agent → Reviewer → Deploy six nodes, one shared state, graph completes itself while you watch loop mode gets disabled the second the task splits into real specialties, one agent stops trying to do everything at once reviewer catches a failure, kicks it straight back to the exact node that broke - not a full restart, no lost context the graph is not a fancier loop - it is the org chart your agents were missing bookmark this and watch it run, then read the article below to see why the timeline just found what production systems already do

leopardracer

53,328 görüntüleme • 2 ay önce

THIS DEVELOPER USED A 10,000+ STAR OBSIDIAN REPO TO TURN HIS GROWING VAULT INTO A SEARCHABLE AI MEMORY SYSTEM most people keep adding notes until the graph looks impressive but nobody can find the exact context when a real task starts new note → ai summary → entity links → backlinks → graph update → claude retrieves the right 3–5 files instead of scanning the entire vault the system connects projects, decisions, research, and unfinished ideas, so old context resurfaces automatically instead of disappearing inside hundreds of folders he built the workflow around Claudian, an open-source repo with 10,000+ GitHub stars that brings Claude Code directly into Obsidian → bookmark this before your next 1,000 notes become another beautiful graph that your AI still cannot use
0:19

THIS DEVELOPER USED A 10,000+ STAR OBSIDIAN REPO TO TURN HIS GROWING VAULT INTO A SEARCHABLE AI MEMORY SYSTEM most people keep adding notes until the graph looks impressive but nobody can find the exact context when a real task starts new note → ai summary → entity links → backlinks → graph update → claude retrieves the right 3–5 files instead of scanning the entire vault the system connects projects, decisions, research, and unfinished ideas, so old context resurfaces automatically instead of disappearing inside hundreds of folders he built the workflow around Claudian, an open-source repo with 10,000+ GitHub stars that brings Claude Code directly into Obsidian → bookmark this before your next 1,000 notes become another beautiful graph that your AI still cannot use

Gipp 🦅

16,393 görüntüleme • 2 ay önce

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.
0:16

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 görüntüleme • 2 ay önce

holy sh*t, someone mapped an entire company into 388 AI agent skills every solo builder can steal the workflow this repo covers 20 domains across engineering, product, marketing, finance, sales, compliance and research the patterns: → challenge your architecture with a CTO persona → turn customer research into a product spec → build with engineering and DevOps skills → run QA and security checks before launch → plan content, SEO, pricing and distribution → model budgets and SaaS metrics → prepare compliance evidence as you grow the architecture: skills define how → agents define the job → personas shape decisions → orchestration connects the work the part solo builders should steal: idea → research → spec → build → test → launch → sell one person can run the whole sequence with a clear job for their agent at every step save this, then build a second brain for your company⭣
0:15

holy sh*t, someone mapped an entire company into 388 AI agent skills every solo builder can steal the workflow this repo covers 20 domains across engineering, product, marketing, finance, sales, compliance and research the patterns: → challenge your architecture with a CTO persona → turn customer research into a product spec → build with engineering and DevOps skills → run QA and security checks before launch → plan content, SEO, pricing and distribution → model budgets and SaaS metrics → prepare compliance evidence as you grow the architecture: skills define how → agents define the job → personas shape decisions → orchestration connects the work the part solo builders should steal: idea → research → spec → build → test → launch → sell one person can run the whole sequence with a clear job for their agent at every step save this, then build a second brain for your company⭣

beamnxw ./

100,405 görüntüleme • 10 gün önce

this is worth more than most five figure courses 16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for: - write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced - the seams that fail that question were never dependencies, so those jobs run at the same time - the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens - want it faster, cut a false edge instead of adding a worker - fan the independent work out, one agent per item, no shared state between them - send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does - make that verifier check a real signal like a passing test, never the worker's own word that it finished - shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset - merge only what came back verified, into one report instead of twenty open chats the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓
0:15

this is worth more than most five figure courses 16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for: - write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced - the seams that fail that question were never dependencies, so those jobs run at the same time - the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens - want it faster, cut a false edge instead of adding a worker - fan the independent work out, one agent per item, no shared state between them - send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does - make that verifier check a real signal like a passing test, never the worker's own word that it finished - shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset - merge only what came back verified, into one report instead of twenty open chats the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓

Argona

157,688 görüntüleme • 2 ay önce

Kimi K3 + GPT-6 Astra can become something bigger than two agents: a Two-Brain AI Operating System the formula: Two-Brain OS = Research Brain + Execution Brain + Shared State + Router + Tools + Verification not two models doing the same job. two specialized brains connected through one persistent system step 1 -> Kimi K3 becomes the research brain. complex research, source comparison, long-context synthesis and strategic planning happen here. its job is to explore the problem and compress the findings into a clear plan. step 2 -> GPT-6 Astra becomes the execution brain. it receives the plan, writes code, operates tools, creates artifacts and turns decisions into finished work. step 3 -> build a shared state layer. store the objective, evidence, decisions, constraints, failed attempts and next action outside the chat. both brains always know what happened and where the system should continue. step 4 -> add the router. uncertainty and open-ended questions move to Kimi K3. execution, tool use and deterministic tasks move to GPT-6 Astra. every job reaches the brain designed to handle it. step 5 -> create the handoff loop: research -> plan -> execute -> inspect -> update state -> continue. if execution reveals missing information, the task returns to Kimi. if the plan is ready, Astra takes control again. step 6 -> verify before completion. tests, source checks, constraints and explicit success criteria decide whether the result ships or returns to the correct brain with a clear failure signal. that is the difference between using two AI models and building a Two-Brain AI Operating System. Kimi K3 expands the search space. GPT-6 Astra converts it into action. shared state preserves progress, the router controls every handoff, tools execute the work and verification decides when the system is actually finished. one model can generate an answer. two specialized brains connected through memory, routing and verification can run an entire workflow. the full Kimi K3 + GPT-6 Astra Two-Brain OS breakdown is below ↓
0:15

Kimi K3 + GPT-6 Astra can become something bigger than two agents: a Two-Brain AI Operating System the formula: Two-Brain OS = Research Brain + Execution Brain + Shared State + Router + Tools + Verification not two models doing the same job. two specialized brains connected through one persistent system step 1 -> Kimi K3 becomes the research brain. complex research, source comparison, long-context synthesis and strategic planning happen here. its job is to explore the problem and compress the findings into a clear plan. step 2 -> GPT-6 Astra becomes the execution brain. it receives the plan, writes code, operates tools, creates artifacts and turns decisions into finished work. step 3 -> build a shared state layer. store the objective, evidence, decisions, constraints, failed attempts and next action outside the chat. both brains always know what happened and where the system should continue. step 4 -> add the router. uncertainty and open-ended questions move to Kimi K3. execution, tool use and deterministic tasks move to GPT-6 Astra. every job reaches the brain designed to handle it. step 5 -> create the handoff loop: research -> plan -> execute -> inspect -> update state -> continue. if execution reveals missing information, the task returns to Kimi. if the plan is ready, Astra takes control again. step 6 -> verify before completion. tests, source checks, constraints and explicit success criteria decide whether the result ships or returns to the correct brain with a clear failure signal. that is the difference between using two AI models and building a Two-Brain AI Operating System. Kimi K3 expands the search space. GPT-6 Astra converts it into action. shared state preserves progress, the router controls every handoff, tools execute the work and verification decides when the system is actually finished. one model can generate an answer. two specialized brains connected through memory, routing and verification can run an entire workflow. the full Kimi K3 + GPT-6 Astra Two-Brain OS breakdown is below ↓

Alex

23,962 görüntüleme • 25 gün önce

Maestro was rebuilt around agents. Not as an AI company. We don’t sell AI products. Maestro Machina is the operating layer underneath the business: -How we write software -Run departments -Keep every product detail, client conversation, and decision in one shared knowledge base Builder agents watch code quality and deployments in a loop that improves itself. Product agents sit inside design, sales, onboarding, and research. Knowledge agents make the company’s memory available to both. Humans still set the intent. Agents industrialize the work. One system. One mission. Build a financial system backed by energy and anchored by Bitcoin.
0:10

Maestro was rebuilt around agents. Not as an AI company. We don’t sell AI products. Maestro Machina is the operating layer underneath the business: -How we write software -Run departments -Keep every product detail, client conversation, and decision in one shared knowledge base Builder agents watch code quality and deployments in a loop that improves itself. Product agents sit inside design, sales, onboarding, and research. Knowledge agents make the company’s memory available to both. Humans still set the intent. Agents industrialize the work. One system. One mission. Build a financial system backed by energy and anchored by Bitcoin.

Maestro

11,384 görüntüleme • 9 gün önce

i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣
0:15

i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣

beamnxw ./

32,741 görüntüleme • 9 gün önce

THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them instead
0:16

THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them instead

Marvin

24,954 görüntüleme • 4 ay önce

this AI engineering breakdown from IBM Technology is pure f*cking treasure it explains the role as a connected system 01 Python and APIs give you the surface to build on: services, data, integrations, and the software around the model 02 RAG and embeddings solve the context layer: finding the right information before the model has to reason about it 03 agents turn that context into work: they choose tools, run steps, and move toward an outcome 04 observability closes the feedback loop: traces, metrics, failures, latency, and the evidence needed to improve the system 05 deployment is where the system becomes real: reliability, constraints, and the conditions that let people actually depend on it the model sits inside this architecture the AI engineer connects every layer into one operating loop: build → retrieve → act → observe → ship save this, then build your solo business with an AI employee ⭣
0:18

this AI engineering breakdown from IBM Technology is pure f*cking treasure it explains the role as a connected system 01 Python and APIs give you the surface to build on: services, data, integrations, and the software around the model 02 RAG and embeddings solve the context layer: finding the right information before the model has to reason about it 03 agents turn that context into work: they choose tools, run steps, and move toward an outcome 04 observability closes the feedback loop: traces, metrics, failures, latency, and the evidence needed to improve the system 05 deployment is where the system becomes real: reliability, constraints, and the conditions that let people actually depend on it the model sits inside this architecture the AI engineer connects every layer into one operating loop: build → retrieve → act → observe → ship save this, then build your solo business with an AI employee ⭣

beamnxw ./

13,547 görüntüleme • 19 gün önce

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.
0:20

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

31,198 görüntüleme • 1 ay önce

this graph is not just a visualization of five AI bots working at once it is a map of shared context, authority and handoffs grok bot becomes interesting when the agents stop behaving like separate chat windows and start operating as one persistent system a chief of staff can route work across a researcher, writer, designer and ops tracker while they share the same cloud environment: browser sessions, terminal, filesystem and authenticated tools the lines between the bots are where the real leverage is - research becomes input for writing - writing becomes input for design - design becomes input for distribution - and the workflow keeps moving while you sleep but every connection also expands the blast radius shared sessions mean shared authority, so the architecture only works safely when: - every bot has a narrow role - irreversible actions require human approval - credentials are granted at the fleet level, not casually per task - persistent state and skills live inside /workspace - the chief of staff coordinates work without becoming an unrestricted superuser the breakthrough is not five AI employees it is one persistent operating system for an autonomous team the article maps out the complete chief-of-staff topology, secure session handoffs, approval boundaries and /workspace survival strategy ↓
0:12

this graph is not just a visualization of five AI bots working at once it is a map of shared context, authority and handoffs grok bot becomes interesting when the agents stop behaving like separate chat windows and start operating as one persistent system a chief of staff can route work across a researcher, writer, designer and ops tracker while they share the same cloud environment: browser sessions, terminal, filesystem and authenticated tools the lines between the bots are where the real leverage is - research becomes input for writing - writing becomes input for design - design becomes input for distribution - and the workflow keeps moving while you sleep but every connection also expands the blast radius shared sessions mean shared authority, so the architecture only works safely when: - every bot has a narrow role - irreversible actions require human approval - credentials are granted at the fleet level, not casually per task - persistent state and skills live inside /workspace - the chief of staff coordinates work without becoming an unrestricted superuser the breakthrough is not five AI employees it is one persistent operating system for an autonomous team the article maps out the complete chief-of-staff topology, secure session handoffs, approval boundaries and /workspace survival strategy ↓

monokern

15,114 görüntüleme • 1 ay önce

a contractor in Shenzhen priced a ¥12,470,900 hospital contract, about $1.7m, in one afternoon and beat firms carrying forty people he explained how he did it: the bid consultancy he used to pay took three days and ¥46,000 for the same envelope. he did this one alone, off one screen, at 11.4% margin, uploaded before the 17:00 cutoff 214 pages of tender documents read, 68 binding clauses pulled out, 9,485 building parts loaded, 14 places found where a duct and a beam sit in the same cubic metre, deepest one 38mm, all of them fixed, 3,318 lines of quantities priced and the package encrypted and uploaded before the 17:00 cutoff this is Graph Engineering: the job gets cut into small nodes, one narrow task each, wired so that one node's output is the next node's input, and any node is allowed to stop the whole run. it turns a model that answers you into a machine that finishes the job: - give every node one job and one output. a node doing two things fails at both and you cannot tell which one broke - put the cheapest rejection first. his qualification node reads clause 7.4, foreign-owned firms barred, and ends the run four seconds in, before anything expensive touches the model - what moves between nodes is a file. the model travels as a model, the quantities as a table, the price as a number - build exactly one loop: the checker finds 14 collisions, the fixer drops the duct 550mm, the checker runs again, and nothing moves on until the count is zero - cap that loop, or a graph will grind on three impossible clashes until the deadline passes - keep one node whose only job is to say no, and give it authority over everything above it - log each node's output on its own, because when the price comes out wrong you need to know which node believed the wrong thing - run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes that earn their keep are the ones that reject, and almost nobody builds those first bookmark this, the full build with all nine nodes and what each one hands to the next is written out in the article ↓
0:16

a contractor in Shenzhen priced a ¥12,470,900 hospital contract, about $1.7m, in one afternoon and beat firms carrying forty people he explained how he did it: the bid consultancy he used to pay took three days and ¥46,000 for the same envelope. he did this one alone, off one screen, at 11.4% margin, uploaded before the 17:00 cutoff 214 pages of tender documents read, 68 binding clauses pulled out, 9,485 building parts loaded, 14 places found where a duct and a beam sit in the same cubic metre, deepest one 38mm, all of them fixed, 3,318 lines of quantities priced and the package encrypted and uploaded before the 17:00 cutoff this is Graph Engineering: the job gets cut into small nodes, one narrow task each, wired so that one node's output is the next node's input, and any node is allowed to stop the whole run. it turns a model that answers you into a machine that finishes the job: - give every node one job and one output. a node doing two things fails at both and you cannot tell which one broke - put the cheapest rejection first. his qualification node reads clause 7.4, foreign-owned firms barred, and ends the run four seconds in, before anything expensive touches the model - what moves between nodes is a file. the model travels as a model, the quantities as a table, the price as a number - build exactly one loop: the checker finds 14 collisions, the fixer drops the duct 550mm, the checker runs again, and nothing moves on until the count is zero - cap that loop, or a graph will grind on three impossible clashes until the deadline passes - keep one node whose only job is to say no, and give it authority over everything above it - log each node's output on its own, because when the price comes out wrong you need to know which node believed the wrong thing - run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes that earn their keep are the ones that reject, and almost nobody builds those first bookmark this, the full build with all nine nodes and what each one hands to the next is written out in the article ↓

Argona

38,189 görüntüleme • 2 ay önce

OpenAI DOTS can take one idea from market research to first outreach and results while you SLEEP > Most people will open it, type one prompt and close the tab. That is the most EXPENSIVE way to use it. Here is the better way, you hand one dot an opportunity and it becomes the orchestrator. It splits the job between five specialists. One validates demand, one sets the offer and pricing, one builds the content, one handles distribution and outreach, and one tracks what worked. Each of them has its own cloud computer and its own browser. They log into your tools once and stay signed in, so your laptop can stay CLOSED. Reading, researching and drafting happen on their own. Anything that sends a message, spends money or overwrites a file stops and waits for your OK. You get a ping in Slack, tap approve, and the work picks back up. You can also set a clock, a morning routine pulls from your apps and drops one brief in Slack before you open your eyes. One objective in, a whole team working on it overnight. Full setup in the article ↓
0:19

OpenAI DOTS can take one idea from market research to first outreach and results while you SLEEP > Most people will open it, type one prompt and close the tab. That is the most EXPENSIVE way to use it. Here is the better way, you hand one dot an opportunity and it becomes the orchestrator. It splits the job between five specialists. One validates demand, one sets the offer and pricing, one builds the content, one handles distribution and outreach, and one tracks what worked. Each of them has its own cloud computer and its own browser. They log into your tools once and stay signed in, so your laptop can stay CLOSED. Reading, researching and drafting happen on their own. Anything that sends a message, spends money or overwrites a file stops and waits for your OK. You get a ping in Slack, tap approve, and the work picks back up. You can also set a clock, a morning routine pulls from your apps and drops one brief in Slack before you open your eyes. One objective in, a whole team working on it overnight. Full setup in the article ↓

slash1s

30,980 görüntüleme • 6 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

32,744 görüntüleme • 2 ay önce

a developer showed me the cleanest multi-agent architecture i've seen. one mission. nobody steps on anyone else. the mission: ship a safe feature. the graph decides what happens next. router reads the mission and asks one question: where should this go? three workers split in parallel. each in their own private work area. researcher finds the evidence. architect designs it. builder creates it. none of them share context. the three workers run independently. everything lands in shared state. facts, decisions, artifacts. one place where the whole picture exists without anyone copying transcripts. then the crew's work converges. integrator combines what was built. reviewer tests quality and safety. human checkpoint approves anything high-impact. then ship. verified output. private context. shared state. that separation is the whole trick. a prompter asks a question. an architect draws a graph. full breakdown with code in the article below.
0:11

a developer showed me the cleanest multi-agent architecture i've seen. one mission. nobody steps on anyone else. the mission: ship a safe feature. the graph decides what happens next. router reads the mission and asks one question: where should this go? three workers split in parallel. each in their own private work area. researcher finds the evidence. architect designs it. builder creates it. none of them share context. the three workers run independently. everything lands in shared state. facts, decisions, artifacts. one place where the whole picture exists without anyone copying transcripts. then the crew's work converges. integrator combines what was built. reviewer tests quality and safety. human checkpoint approves anything high-impact. then ship. verified output. private context. shared state. that separation is the whole trick. a prompter asks a question. an architect draws a graph. full breakdown with code in the article below.

rvaniaaa

27,465 görüntüleme • 1 ay önce

I BUILT A 6-PERSON COMPANY WITH AI AGENTS. I split the business into 6 jobs: -> RESEARCH finds opportunities -> OUTREACH finds prospects -> SALES qualifies leads -> BUILDER does the work -> SUPPORT handles customers -> FINANCE tracks the money one agent finishes -> the next one starts. anything risky - sending, spending, publishing - stops for human approval. so instead of asking ChatGPT to “help with my business,” you basically become the manager of a tiny AI workforce. 6 agents -> 1 workflow -> human approval -> shipped work and this is the interesting part: you don’t need to build some $1M autonomous company for this to make sense. getting one boring agent to solve one painful business problem and finding someone willing to pay $500–$2,000 for it is a much more realistic starting point. I broke down exactly how I’d go from $0 -> first $2,000 with AI agents below.
0:10

I BUILT A 6-PERSON COMPANY WITH AI AGENTS. I split the business into 6 jobs: -> RESEARCH finds opportunities -> OUTREACH finds prospects -> SALES qualifies leads -> BUILDER does the work -> SUPPORT handles customers -> FINANCE tracks the money one agent finishes -> the next one starts. anything risky - sending, spending, publishing - stops for human approval. so instead of asking ChatGPT to “help with my business,” you basically become the manager of a tiny AI workforce. 6 agents -> 1 workflow -> human approval -> shipped work and this is the interesting part: you don’t need to build some $1M autonomous company for this to make sense. getting one boring agent to solve one painful business problem and finding someone willing to pay $500–$2,000 for it is a much more realistic starting point. I broke down exactly how I’d go from $0 -> first $2,000 with AI agents below.

kiosa

24,523 görüntüleme • 1 ay önce

THIS GUY TURNED HIMSELF INTO AN AI GIRL IN ONE SECOND. THE WORKFLOW BEHIND IT CAN RUN A $5,000/MONTH FANVUE PAGE IN 40 MINUTES A DAY 00:01 he raises his arms and instantly turns into an AI girl sitting in the same chair, ready to stream without ever showing his real face Claude creates the name, personality, captions and replies. one prompt can generate a full week of content while keeping the same character across TikTok and Fanvue ComfyUI with Flux builds the face and photo library. Kling 3.0 animates the images, then CapCut turns them into 30 to 50 short videos in one afternoon three daily streams and one clip reaching 400,000 views can send 70 buyers into a $15 Fanvue subscription. paid messages and tips are what push the page past $5,000 he stays behind the camera, Claude runs the brain and the AI girl becomes a business that works every day
0:16

THIS GUY TURNED HIMSELF INTO AN AI GIRL IN ONE SECOND. THE WORKFLOW BEHIND IT CAN RUN A $5,000/MONTH FANVUE PAGE IN 40 MINUTES A DAY 00:01 he raises his arms and instantly turns into an AI girl sitting in the same chair, ready to stream without ever showing his real face Claude creates the name, personality, captions and replies. one prompt can generate a full week of content while keeping the same character across TikTok and Fanvue ComfyUI with Flux builds the face and photo library. Kling 3.0 animates the images, then CapCut turns them into 30 to 50 short videos in one afternoon three daily streams and one clip reaching 400,000 views can send 70 buyers into a $15 Fanvue subscription. paid messages and tips are what push the page past $5,000 he stays behind the camera, Claude runs the brain and the AI girl becomes a business that works every day

Gipp 🦅

132,914 görüntüleme • 3 ay önce

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓
0:20

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓

SCOTTY BEAM

4,827,162 görüntüleme • 1 ay önce

Research gets messy very quickly when one AI is trying to do everything alone. Grok Build’s new /deep-research command is built for the bigger jobs. It sends multiple agents into the work at once, but with structure. Plan. Research. Verify. Report. That means the system can split the task, chase evidence, cross-check findings, and come back with cited output instead of one lonely model guessing in a corner. The geeky kind of command people will start overusing immediately. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné
0:17

Research gets messy very quickly when one AI is trying to do everything alone. Grok Build’s new /deep-research command is built for the bigger jobs. It sends multiple agents into the work at once, but with structure. Plan. Research. Verify. Report. That means the system can split the task, chase evidence, cross-check findings, and come back with cited output instead of one lonely model guessing in a corner. The geeky kind of command people will start overusing immediately. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

36,005 görüntüleme • 2 ay önce