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A developer opened a law firm’s token bill and realized 20 users were burning $61,920 just to ask questions their files could answer offline. $61,920 cloud TCO. $23,180 local TCO. 4.12-month break-even. 20 employees. 0 client files uploaded. The old RAG pitch breaks when legal files, medical notes, and...

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Lummox

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25,315 Aufrufe • vor 1 Monat •via X (Twitter)

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Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud
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Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

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THE OBSIDIAN GRAPH IS NOT A PRETTY NOTE MAP, IT IS A SELF-MAINTAINING AI WIKI THAT CAN TURN 120 SAVED SOURCES INTO 700 LINKED PAGES WHILE YOU ONLY KEEP ADDING NEW MATERIAL 00:11 the graph opens and the trick becomes obvious: every dot is a saved idea, every cluster is a topic, and every line is context the AI no longer has to rebuild from scratch every morning most people use chatgpt like a disposable memory slot. they upload 1 pdf, ask 3 questions, close the tab, and the next day the model knows nothing unless they feed it the same context again karpathy’s LLM wiki pattern changes the game. the model reads each new source, pulls out claims, updates old pages, creates entity files, links concepts, and marks places where newer info breaks older notes with claude code and obsidian, a single clipped article can expand into 8-20 markdown files: source summary, concept pages, people, tools, comparisons, open questions, and a running index after 60 sources, this is no longer “note taking.” it becomes a private research machine that remembers your old thinking, catches repeated angles, and gives you cited answers from your own archive bookmark this before your notes turn into a graveyard again
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THE OBSIDIAN GRAPH IS NOT A PRETTY NOTE MAP, IT IS A SELF-MAINTAINING AI WIKI THAT CAN TURN 120 SAVED SOURCES INTO 700 LINKED PAGES WHILE YOU ONLY KEEP ADDING NEW MATERIAL 00:11 the graph opens and the trick becomes obvious: every dot is a saved idea, every cluster is a topic, and every line is context the AI no longer has to rebuild from scratch every morning most people use chatgpt like a disposable memory slot. they upload 1 pdf, ask 3 questions, close the tab, and the next day the model knows nothing unless they feed it the same context again karpathy’s LLM wiki pattern changes the game. the model reads each new source, pulls out claims, updates old pages, creates entity files, links concepts, and marks places where newer info breaks older notes with claude code and obsidian, a single clipped article can expand into 8-20 markdown files: source summary, concept pages, people, tools, comparisons, open questions, and a running index after 60 sources, this is no longer “note taking.” it becomes a private research machine that remembers your old thinking, catches repeated angles, and gives you cited answers from your own archive bookmark this before your notes turn into a graveyard again

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THIS GUY TURNED OBSIDIAN INTO A DIGITAL TWIN OF HIS OWN BRAIN, 400 PLUS NODES, 6 KNOWLEDGE CLUSTERS, 1 LOCAL VAULT, AND A MAP THAT GETS SMARTER WITH EVERY NOTE 00:05 the graph begins as one dense sphere, then individual paths light up across the screen, showing how a single note can connect to dozens of ideas instead of disappearing inside another folder. most people store information in straight lines, one file after another. his vault grows like a network, research connects to projects, projects connect to decisions, and old notes keep feeding new work months later. after 100 notes, it still feels like storage. around 300, clusters begin exposing repeated interests, unfinished threads, and topics that keep returning even after he stops thinking about them. with claude connected, the network becomes usable context. instead of pasting 20 files into one chat, an agent can follow 5 linked notes, pull original sources, compare old decisions, and write the result back into the same vault. a real second brain is not the pretty graph. it is a local system where every new thought strengthens old connections, buried ideas come back when needed, and knowledge keeps compounding without leaving your machine.
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THIS GUY TURNED OBSIDIAN INTO A DIGITAL TWIN OF HIS OWN BRAIN, 400 PLUS NODES, 6 KNOWLEDGE CLUSTERS, 1 LOCAL VAULT, AND A MAP THAT GETS SMARTER WITH EVERY NOTE 00:05 the graph begins as one dense sphere, then individual paths light up across the screen, showing how a single note can connect to dozens of ideas instead of disappearing inside another folder. most people store information in straight lines, one file after another. his vault grows like a network, research connects to projects, projects connect to decisions, and old notes keep feeding new work months later. after 100 notes, it still feels like storage. around 300, clusters begin exposing repeated interests, unfinished threads, and topics that keep returning even after he stops thinking about them. with claude connected, the network becomes usable context. instead of pasting 20 files into one chat, an agent can follow 5 linked notes, pull original sources, compare old decisions, and write the result back into the same vault. a real second brain is not the pretty graph. it is a local system where every new thought strengthens old connections, buried ideas come back when needed, and knowledge keeps compounding without leaving your machine.

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Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.
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Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

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This guy put an AI data center into a box that fits on a desk It doesn't look like a serious machine It looks like a mini PC you could hide behind a monitor But with enough unified memory it can run local models, answer over your own files, power agents and keep private work off someone else's servers That sounds like a hobby setup until you look at what people are actually renting every month Claude subscription ChatGPT subscription API credits cloud GPU time usage limits private files uploaded into third-party tools For years the deal was simple: if you wanted intelligence, you rented it Now a Mac Mini M4 Pro or a 128GB AMD mini PC can handle a big part of everyday AI work locally Ollama runs the models Open WebUI gives you the chat interface Tailscale lets you reach it from your phone or laptop without exposing it to the open internet The cloud still wins for the hardest tasks But most daily AI work is not frontier research It is reading documents, summarizing files, drafting, coding helpers, search, notes and private workflows Most people will keep paying rent because it is convenient Others are realizing the assistant they rent every month can become hardware they own save this
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This guy put an AI data center into a box that fits on a desk It doesn't look like a serious machine It looks like a mini PC you could hide behind a monitor But with enough unified memory it can run local models, answer over your own files, power agents and keep private work off someone else's servers That sounds like a hobby setup until you look at what people are actually renting every month Claude subscription ChatGPT subscription API credits cloud GPU time usage limits private files uploaded into third-party tools For years the deal was simple: if you wanted intelligence, you rented it Now a Mac Mini M4 Pro or a 128GB AMD mini PC can handle a big part of everyday AI work locally Ollama runs the models Open WebUI gives you the chat interface Tailscale lets you reach it from your phone or laptop without exposing it to the open internet The cloud still wins for the hardest tasks But most daily AI work is not frontier research It is reading documents, summarizing files, drafting, coding helpers, search, notes and private workflows Most people will keep paying rent because it is convenient Others are realizing the assistant they rent every month can become hardware they own save this

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THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.
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THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

kocer

30,403 Aufrufe • vor 2 Monaten

A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid
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A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

Gipp 🦅

458,361 Aufrufe • vor 3 Monaten

KARPATHY'S SECOND BRAIN REWRITES YOUR NOTES EVERY TIME YOU LEARN SOMETHING NEW most AI note setups are just RAG you ask, it digs through your files, answers, and forgets nothing carries over every query starts from zero Karpathy's llm-wiki works differently the AI keeps a living wiki inside Obsidian every source you drop in gets: > read and summarized > cross-linked to what you already have > checked for contradictions > folded into the running synthesis the knowledge compounds instead of resetting now the part people miss: a wiki like this is a product > deep research reports to founders go for $2-5k each > a due diligence package on a niche runs $5-10k > a paid newsletter off your synthesis pages clears $3-4k/mo at a few hundred subs your job is to curate sources and ask good questions the AI does the bookkeeping no human keeps up with - Obsidian is the IDE - the LLM is the programmer - your wiki is the codebase save this before your notes pile any higher 👇
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KARPATHY'S SECOND BRAIN REWRITES YOUR NOTES EVERY TIME YOU LEARN SOMETHING NEW most AI note setups are just RAG you ask, it digs through your files, answers, and forgets nothing carries over every query starts from zero Karpathy's llm-wiki works differently the AI keeps a living wiki inside Obsidian every source you drop in gets: > read and summarized > cross-linked to what you already have > checked for contradictions > folded into the running synthesis the knowledge compounds instead of resetting now the part people miss: a wiki like this is a product > deep research reports to founders go for $2-5k each > a due diligence package on a niche runs $5-10k > a paid newsletter off your synthesis pages clears $3-4k/mo at a few hundred subs your job is to curate sources and ask good questions the AI does the bookkeeping no human keeps up with - Obsidian is the IDE - the LLM is the programmer - your wiki is the codebase save this before your notes pile any higher 👇

Mr. Buzzoni

18,500 Aufrufe • vor 1 Monat

THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation
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THIS CHINESE DEVELOPER'S OBSIDIAN BRAIN MAPS EVERY CLIENT CONVERSATION - ZERO EMPLOYEES, $40,000/MONTH, CATCHES PROBLEMS BEFORE CLIENTS DO every node on screen is a conversation - green for happy clients, yellow for neutral, red for problems that need attention - the brain sees the emotional state of the entire business in real time this is not a CRM, not a dashboard, not a spreadsheet - it's a living map of every client interaction that fires connections and surfaces patterns no human account manager would ever catch when a cluster starts turning yellow the brain flags it before the client sends the complaint - when a green cluster grows it identifies what's working and replicates it across every other account automatically he replaced a $15,000/month account management team with one Obsidian brain that never sleeps, never misses a message and never needs a performance review no employees, no contractors, no standups, no payroll - just one developer, one brain and one delivery system doing the work of 10 people $40,000/month - solo - from a system that gets smarter every single conversation

Noisy

42,207 Aufrufe • vor 3 Monaten

Fable 5 and Karpathy's knowledge base trick are basically a cheat code. Someone just dropped a full walkthrough on building the most powerful second brain ever Andrej Karpathy shared a simple idea: use an LLM to build personal knowledge bases. Index your sources, let the model organize them, and use Obsidian as the front end. Someone just built the full thing with Claude Code and Fable 5, and walked through every step You drop your sources into a folder (YouTube transcripts, PDFs, articles, meeting notes) and Claude Code reads them, splits them into linked wiki pages, and builds a living second brain that connects everything. It grows on its own as you add more. Where Fable 5 pulls ahead: it found a connection buried across two sources you'd miss reading them separately. And asked to turn the messy web of links into something a beginner could understand, it produced clean, simple HTML in one shot. The same task took a full day on Opus 4.8 and came out worse. The best part: it's all just markdown files with routing. So it's not locked to Claude Code. Plug it into Codex or any agent you want. Give your AI a real knowledge base, and it stops guessing and starts knowing. Bookmark this.
14:34

Fable 5 and Karpathy's knowledge base trick are basically a cheat code. Someone just dropped a full walkthrough on building the most powerful second brain ever Andrej Karpathy shared a simple idea: use an LLM to build personal knowledge bases. Index your sources, let the model organize them, and use Obsidian as the front end. Someone just built the full thing with Claude Code and Fable 5, and walked through every step You drop your sources into a folder (YouTube transcripts, PDFs, articles, meeting notes) and Claude Code reads them, splits them into linked wiki pages, and builds a living second brain that connects everything. It grows on its own as you add more. Where Fable 5 pulls ahead: it found a connection buried across two sources you'd miss reading them separately. And asked to turn the messy web of links into something a beginner could understand, it produced clean, simple HTML in one shot. The same task took a full day on Opus 4.8 and came out worse. The best part: it's all just markdown files with routing. So it's not locked to Claude Code. Plug it into Codex or any agent you want. Give your AI a real knowledge base, and it stops guessing and starts knowing. Bookmark this.

Shadow Nick

47,058 Aufrufe • vor 2 Monaten

THIS DEVELOPER TURNED A $599 MAC MINI AND $110 OF EXTRA HARDWARE INTO A PRIVATE AI COMPUTER THAT FITS IN A BACKPACK 00:49 he takes the complete setup out of the bag, connects it to a nearby screen and restores the same desktop, files and running tools in less than a minute. instead of syncing a new laptop every time he moves, the mac mini carries the original obsidian vault, claude code sessions, local models and unfinished agent tasks between every desk. the 10,000mah battery can keep the machine running for roughly 2 to 4 hours during writing, research and file organization, or around 60 to 90 minutes under heavier transcription and local ai workloads. the full build costs close to $710 if a screen is already available. that includes the mac mini, a 55w battery, the side mount and the cables needed to connect it to an ipad, hotel tv or office monitor. one box can carry 12,000 notes, 300gb of private files and several active agents without sending the data to another cloud account or rebuilding the workspace every time he changes locations.
1:05

THIS DEVELOPER TURNED A $599 MAC MINI AND $110 OF EXTRA HARDWARE INTO A PRIVATE AI COMPUTER THAT FITS IN A BACKPACK 00:49 he takes the complete setup out of the bag, connects it to a nearby screen and restores the same desktop, files and running tools in less than a minute. instead of syncing a new laptop every time he moves, the mac mini carries the original obsidian vault, claude code sessions, local models and unfinished agent tasks between every desk. the 10,000mah battery can keep the machine running for roughly 2 to 4 hours during writing, research and file organization, or around 60 to 90 minutes under heavier transcription and local ai workloads. the full build costs close to $710 if a screen is already available. that includes the mac mini, a 55w battery, the side mount and the cables needed to connect it to an ipad, hotel tv or office monitor. one box can carry 12,000 notes, 300gb of private files and several active agents without sending the data to another cloud account or rebuilding the workspace every time he changes locations.

Gipp 🦅

30,550 Aufrufe • vor 2 Monaten

I tried MUZIM by OpenSoul on my Mac this week. Most “AI for your files” tools want you to upload everything into someone else’s cloud. MUZIM is different. It is a local AI for your photo and video library. Not another chatbot. Your raw files stay on your machine. I imported a messy media dump and searched the way I actually remember things. Not filenames. Vibes. I typed “that rainy cafe by the window.” It found the shot. It can even land on a moment inside a video. And after the model downloads, that kind of search keeps working offline. Then the Smart Organization kicked in. I did not rebuild folders. MUZIM built Collections on its own from the dump. Nature, outfits, travel vibes. The library went from chaos to something I could browse. The part that surprised me most is what happens next. Once the library is organized, the AI Agent helps turn it into something I can actually ship. Captions. An X thread. A short highlight concept with hooks. Cold storage becomes a private workspace. You can also plug in your own Claude or GPT key if you want cloud AI on top of local files. Still your library. Still your control. This is the kind of AI I want. More understanding. Less uploading everything to the cloud. Try it:
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I tried MUZIM by OpenSoul on my Mac this week. Most “AI for your files” tools want you to upload everything into someone else’s cloud. MUZIM is different. It is a local AI for your photo and video library. Not another chatbot. Your raw files stay on your machine. I imported a messy media dump and searched the way I actually remember things. Not filenames. Vibes. I typed “that rainy cafe by the window.” It found the shot. It can even land on a moment inside a video. And after the model downloads, that kind of search keeps working offline. Then the Smart Organization kicked in. I did not rebuild folders. MUZIM built Collections on its own from the dump. Nature, outfits, travel vibes. The library went from chaos to something I could browse. The part that surprised me most is what happens next. Once the library is organized, the AI Agent helps turn it into something I can actually ship. Captions. An X thread. A short highlight concept with hooks. Cold storage becomes a private workspace. You can also plug in your own Claude or GPT key if you want cloud AI on top of local files. Still your library. Still your control. This is the kind of AI I want. More understanding. Less uploading everything to the cloud. Try it:

Robert Scoble

63,716 Aufrufe • vor 17 Tagen

THIS GUY BUILT A BUSINESS SECOND BRAIN WITH CLAUDE CODE + OBSIDIAN IN 3 STEPS Most teams do not need another Notion workspace. They need a place where the company can remember how it works. The video shows a simple setup: 1. Create one empty folder called second brain. 2. Split it into 3 buckets: raw new knowledge wiki 3. Let Claude Code turn messy company material into connected notes. The useful part is the separation. Raw is where your existing stuff goes: SOPs, sales docs, process notes, client delivery checklists, old Loom summaries, onboarding docs. New knowledge is where fresh outside material lands: articles, clips, tactics, examples, market notes. Wiki is the cleaned version: concepts, roles, processes, SOPs, gaps, reusable decisions. That is where Claude Code becomes more useful than a normal chat window. Instead of asking it to remember random context forever, you give it a folder it can read, edit, and reorganize. Then Obsidian becomes the human interface. The Obsidian Web Clipper captures useful pages into the vault. Claude Code ingests them. The wiki gets updated. Then you can ask questions like: “Does my current workflow actually hold up?” That is the real point. Not “AI notes.” A business memory system that can compare what you do today against new information tomorrow. The caveat: this is not magic company intelligence. If your raw docs are vague, outdated, or full of tribal knowledge, Claude will organize weak inputs into cleaner weak outputs. You still need naming rules, review habits, and someone responsible for deleting junk. But the setup is refreshingly practical. Folder first. Clipper second. Claude Code as the maintainer. No giant knowledge base migration. No complex setup. Just a local vault that can slowly turn scattered business memory into something searchable, editable, and actually reusable.
1:25

THIS GUY BUILT A BUSINESS SECOND BRAIN WITH CLAUDE CODE + OBSIDIAN IN 3 STEPS Most teams do not need another Notion workspace. They need a place where the company can remember how it works. The video shows a simple setup: 1. Create one empty folder called second brain. 2. Split it into 3 buckets: raw new knowledge wiki 3. Let Claude Code turn messy company material into connected notes. The useful part is the separation. Raw is where your existing stuff goes: SOPs, sales docs, process notes, client delivery checklists, old Loom summaries, onboarding docs. New knowledge is where fresh outside material lands: articles, clips, tactics, examples, market notes. Wiki is the cleaned version: concepts, roles, processes, SOPs, gaps, reusable decisions. That is where Claude Code becomes more useful than a normal chat window. Instead of asking it to remember random context forever, you give it a folder it can read, edit, and reorganize. Then Obsidian becomes the human interface. The Obsidian Web Clipper captures useful pages into the vault. Claude Code ingests them. The wiki gets updated. Then you can ask questions like: “Does my current workflow actually hold up?” That is the real point. Not “AI notes.” A business memory system that can compare what you do today against new information tomorrow. The caveat: this is not magic company intelligence. If your raw docs are vague, outdated, or full of tribal knowledge, Claude will organize weak inputs into cleaner weak outputs. You still need naming rules, review habits, and someone responsible for deleting junk. But the setup is refreshingly practical. Folder first. Clipper second. Claude Code as the maintainer. No giant knowledge base migration. No complex setup. Just a local vault that can slowly turn scattered business memory into something searchable, editable, and actually reusable.

kocer

16,642 Aufrufe • vor 2 Monaten

HE TURNED A $599 MAC MINI INTO A CLAUDE SECOND BRAIN USING OBSIDIAN, PYTHON AND A FEW ALWAYS-ON LOOPS not a fancy SaaS dashboard. not another chat tab. a tiny box on a desk that can pull lectures, transcribe audio, read articles, clean notes and write everything into a local vault while he sleeps Steve Jobs wanted the Mac mini to be the small affordable Mac anyone could put on a desk. now that same tiny Apple box can run Claude loops 24/7, process 100+ saved links, turn 2-hour lectures into structured notes, and send a morning brief for a few dollars in API calls the real trick is the loop. trigger, do, verify, retry, stop. Claude does the thinking, Obsidian stores the memory, and the Mac mini keeps running even when nobody is touching the keyboard bookmark this before you think a second brain needs another $29/month app
0:21

HE TURNED A $599 MAC MINI INTO A CLAUDE SECOND BRAIN USING OBSIDIAN, PYTHON AND A FEW ALWAYS-ON LOOPS not a fancy SaaS dashboard. not another chat tab. a tiny box on a desk that can pull lectures, transcribe audio, read articles, clean notes and write everything into a local vault while he sleeps Steve Jobs wanted the Mac mini to be the small affordable Mac anyone could put on a desk. now that same tiny Apple box can run Claude loops 24/7, process 100+ saved links, turn 2-hour lectures into structured notes, and send a morning brief for a few dollars in API calls the real trick is the loop. trigger, do, verify, retry, stop. Claude does the thinking, Obsidian stores the memory, and the Mac mini keeps running even when nobody is touching the keyboard bookmark this before you think a second brain needs another $29/month app

Ridark

65,142 Aufrufe • vor 2 Monaten

THIS GUY OPENED A TINY PC AND SHOWED HOW A $1,800 GMKTEC EVO-X2 TURNS INTO A LOCAL AI SERVER not a cloud dashboard. not another $200/mo subscription. just one box on a desk, upgraded with storage, cleaner airflow, faster networking, and pointed at real local models. the numbers are what make it interesting. Claude Code Max is $200/mo, ChatGPT Pro is $200/mo, Cursor is another $20/mo. that is $5,040/year just to rent tools most people use for coding, research, summaries, and agents. EVO-X2 flips the setup because it has 128GB unified memory, Ryzen AI Max+ 395, and up to 110GB usable as VRAM on Linux. enough room for Qwen3-Coder 30B, Llama 70B, and 200B-class local models when speed is not the bottleneck. so the workflow becomes simple: buy the box once, install Linux, run the model locally, connect the CLI, and let the machine handle jobs from your own network instead of sending every request to the cloud. this is where the real edge starts. overnight code audits, private document search, local RAG, scraping agents, transcription, file cleanup, all running while the token meter stays at zero. frontier models still win for the hardest prompts. but for daily automation, coding, research, and private workflows, local AI is already moving from nerd setup to obvious business math. the next flex won’t be “which AI plan do you pay for?” it’ll be “what’s running under your desk?”
1:09

THIS GUY OPENED A TINY PC AND SHOWED HOW A $1,800 GMKTEC EVO-X2 TURNS INTO A LOCAL AI SERVER not a cloud dashboard. not another $200/mo subscription. just one box on a desk, upgraded with storage, cleaner airflow, faster networking, and pointed at real local models. the numbers are what make it interesting. Claude Code Max is $200/mo, ChatGPT Pro is $200/mo, Cursor is another $20/mo. that is $5,040/year just to rent tools most people use for coding, research, summaries, and agents. EVO-X2 flips the setup because it has 128GB unified memory, Ryzen AI Max+ 395, and up to 110GB usable as VRAM on Linux. enough room for Qwen3-Coder 30B, Llama 70B, and 200B-class local models when speed is not the bottleneck. so the workflow becomes simple: buy the box once, install Linux, run the model locally, connect the CLI, and let the machine handle jobs from your own network instead of sending every request to the cloud. this is where the real edge starts. overnight code audits, private document search, local RAG, scraping agents, transcription, file cleanup, all running while the token meter stays at zero. frontier models still win for the hardest prompts. but for daily automation, coding, research, and private workflows, local AI is already moving from nerd setup to obvious business math. the next flex won’t be “which AI plan do you pay for?” it’ll be “what’s running under your desk?”

Gipp 🦅

13,331 Aufrufe • vor 2 Monaten

Andrej Karpathy keeps his entire second brain in Obsidian 1 folder of plain text files, nothing else. Here’s the exact setup, free, that turns 1 vault into a brain you can actually query. Why most “second brains” die: Notion is a database you forget to open, ChatGPT memory wipes itself after 30 messages, and both lock your notes inside their cloud. Karpathy rule is the opposite: your notes are the 1 dataset nobody else on earth has, so keep them in plain markdown, own them forever, and let the AI read them later. 3 phases. Phase 1 — Capture with 0 friction. Install Obsidian free, local, 1 hotkey to a daily note. Dump everything into it: ideas, links, quotes, 2-line thoughts, no folders, no tags, no system. Target is 5 notes a day. Phase 2 — Let the graph connect itself. Install Smart Connections and it runs embeddings on every note you’ve ever written, so 2 notes you wrote 4 months apart and never linked by hand suddenly surface as neighbors. That’s the fireworks in the 3D graph each burst is a cluster of ideas your brain forgot it owned. Phase 3 — Query your brain. Point Claude at the vault and ask: “What did I actually think about X across all my notes?” It answers from 1,200 of your own notes, not the internet your taste, your edge, your dataset. The realistic curve looks like this: Week 1 — 40 notes, graph looks empty, feels like a waste. Month 1 — 300 notes, first real cluster forms. Month 3 — 1,200 notes, and the embeddings start surfacing links you’d long forgotten. Month 6 — you stop googling your own past ideas and just ask the vault. Before — 9 tabs, 3 note apps, 0 memory. After — 1 folder, 1,200 notes, 1 brain that compounds. The gap between people who consume and people who compound isn’t talent, it’s architecture. You build your own life so choose the right path.
0:31

Andrej Karpathy keeps his entire second brain in Obsidian 1 folder of plain text files, nothing else. Here’s the exact setup, free, that turns 1 vault into a brain you can actually query. Why most “second brains” die: Notion is a database you forget to open, ChatGPT memory wipes itself after 30 messages, and both lock your notes inside their cloud. Karpathy rule is the opposite: your notes are the 1 dataset nobody else on earth has, so keep them in plain markdown, own them forever, and let the AI read them later. 3 phases. Phase 1 — Capture with 0 friction. Install Obsidian free, local, 1 hotkey to a daily note. Dump everything into it: ideas, links, quotes, 2-line thoughts, no folders, no tags, no system. Target is 5 notes a day. Phase 2 — Let the graph connect itself. Install Smart Connections and it runs embeddings on every note you’ve ever written, so 2 notes you wrote 4 months apart and never linked by hand suddenly surface as neighbors. That’s the fireworks in the 3D graph each burst is a cluster of ideas your brain forgot it owned. Phase 3 — Query your brain. Point Claude at the vault and ask: “What did I actually think about X across all my notes?” It answers from 1,200 of your own notes, not the internet your taste, your edge, your dataset. The realistic curve looks like this: Week 1 — 40 notes, graph looks empty, feels like a waste. Month 1 — 300 notes, first real cluster forms. Month 3 — 1,200 notes, and the embeddings start surfacing links you’d long forgotten. Month 6 — you stop googling your own past ideas and just ask the vault. Before — 9 tabs, 3 note apps, 0 memory. After — 1 folder, 1,200 notes, 1 brain that compounds. The gap between people who consume and people who compound isn’t talent, it’s architecture. You build your own life so choose the right path.

Spike 1%

16,390 Aufrufe • vor 2 Monaten

Obsidian + Claude Code turned a 4,300-note vault into a second brain that answers back. Most people use Obsidian as a graveyard. You save 40 highlights a week, link nothing, and reread maybe 2% of it. The knowledge is there the retrieval is dead. Claude Code fixes the retrieval, because your vault is just a folder of markdown files and Claude Code lives in folders. The whole setup takes 20 minutes: Open a terminal inside your Obsidian folder and run Claude Code no plugins, no API glue, no export step. It reads all 4,300 files natively. Then drop a CLAUDE.md at the vault root explaining your structure: where daily notes live, how you tag, what your MOCs are. Now every session starts with context instead of chaos. From there you just talk to your vault: > "find every note where I mentioned churn and write a summary with backlinks" > "read my last 30 daily notes and list the 5 ideas I keep circling but never ship" > "build a MOC for everything tagged startup-ideas and link the orphan notes into it" It writes new notes straight into the vault with proper [[wikilinks]], and they show up in your graph view 10 seconds later. The boring layer automates too one command cleans broken links, one merges duplicates, one turns 6 months of meeting notes into a single decisions log. Work that used to eat a Sunday now runs while you make coffee. Before: 4,300 notes, 900 orphans, search by keyword and prayer. After: an agent that read everything you ever wrote and drafts in your own voice, from your own sources, with receipts. People pay $30/month for AI note apps with 10% of this. You already own the other 90%. Your notes stopped being storage. They started being staff.
0:55

Obsidian + Claude Code turned a 4,300-note vault into a second brain that answers back. Most people use Obsidian as a graveyard. You save 40 highlights a week, link nothing, and reread maybe 2% of it. The knowledge is there the retrieval is dead. Claude Code fixes the retrieval, because your vault is just a folder of markdown files and Claude Code lives in folders. The whole setup takes 20 minutes: Open a terminal inside your Obsidian folder and run Claude Code no plugins, no API glue, no export step. It reads all 4,300 files natively. Then drop a CLAUDE.md at the vault root explaining your structure: where daily notes live, how you tag, what your MOCs are. Now every session starts with context instead of chaos. From there you just talk to your vault: > "find every note where I mentioned churn and write a summary with backlinks" > "read my last 30 daily notes and list the 5 ideas I keep circling but never ship" > "build a MOC for everything tagged startup-ideas and link the orphan notes into it" It writes new notes straight into the vault with proper [[wikilinks]], and they show up in your graph view 10 seconds later. The boring layer automates too one command cleans broken links, one merges duplicates, one turns 6 months of meeting notes into a single decisions log. Work that used to eat a Sunday now runs while you make coffee. Before: 4,300 notes, 900 orphans, search by keyword and prayer. After: an agent that read everything you ever wrote and drafts in your own voice, from your own sources, with receipts. People pay $30/month for AI note apps with 10% of this. You already own the other 90%. Your notes stopped being storage. They started being staff.

Spike 1%

18,223 Aufrufe • vor 1 Monat

THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend. the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute. that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system. the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid. the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts. point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend. bookmark this and read the article below
0:47

THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend. the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute. that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system. the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid. the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts. point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend. bookmark this and read the article below

Gipp 🦅

20,031 Aufrufe • vor 2 Monaten

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
9:10

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 Aufrufe • vor 4 Monaten

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 Aufrufe • vor 2 Monaten