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It is becoming f*cking dangerous: Claude Code has kept its own memory folder since February and you've probably never opened it 1. Open any project in Claude Code and type /memory 2. Check the auto memory toggle, it is on by default in local sessions 3. Pick the option...

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plusxbt

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19,250 görüntüleme • 4 gün önce •via X (Twitter)

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Bober_smart profil fotoğrafı
Bober_smart4 gün önce

I recommend this setting to everyone

plusxbt profil fotoğrafı
plusxbt4 gün önce

🫡

The SI Maximalist profil fotoğrafı
The SI Maximalist4 gün önce

The memory folder needs a proper introduction.

Prior profil fotoğrafı
Prior4 gün önce

the 200 line cap is the part to watch. once MEMORY.md grows past it, every line after 200 stops loading and claude just quietly forgets those. worth pruning it every few weeks

Leo profil fotoğrafı
Leo4 gün önce

A memory folder that fills itself is useful until stale notes start steering the model. Do you prune it by hand, or let it accumulate?

Nguyễn Chí Trọng Nghĩa profil fotoğrafı
Nguyễn Chí Trọng Nghĩa4 gün önce

Vào /memory xem thử mới biết Claude Code nhớ dai từ tháng 2, kiểu bạn thân thuộc lòng chuyện cũ 🙂

John Rood profil fotoğrafı
John Rood4 gün önce

yes, and the reason is that the folder stores conclusions, not evidence. a line saying "use X" carries no date and no reason, so nothing in it can go stale on its own. that gap is the whole problem i built memoryrouter around: the decision, the why and the date travel together or you just inherit yesterday's guess.

小樱聊车知识多 profil fotoğrafı
小樱聊车知识多4 gün önce

made the button bigger, cut the radius, entire layout just works better

爱车人的日常频道 profil fotoğrafı
爱车人的日常频道3 gün önce

Next pour was so forget when the ground must already be bitter my okay found no grinding

Atenov int. profil fotoğrafı
Atenov int.4 gün önce

How many people actually read what Claude stored about their workflows before sharing a project folder with someone else?

Benzer Videolar

Boris Cherny gave Claude Code a memory that has no database, no indexing, no setup, and 80% of Anthropic runs on it a CLAUDE.md "gets automatically read into context at the start of every session" 12:48 - CLAUDE.md loads into memory every session 13:15 - Keep memory short or it burns your context 15:48 - Memory stacks from you to your whole company 17:30 - The memory command shows what Claude remembers 19:33 - Hit pound and Claude remembers it forever the whole memory layer is files → drop a CLAUDE.md in your repo and Claude reads it in on every session → nest more in subfolders and it pulls them only where it works → stack them personal to project to enterprise so one config serves the whole team → hit the pound key mid task and Claude writes the new fact into memory itself the best coding agent's memory was never a database, it was markdown you can open and edit
27:26

Boris Cherny gave Claude Code a memory that has no database, no indexing, no setup, and 80% of Anthropic runs on it a CLAUDE.md "gets automatically read into context at the start of every session" 12:48 - CLAUDE.md loads into memory every session 13:15 - Keep memory short or it burns your context 15:48 - Memory stacks from you to your whole company 17:30 - The memory command shows what Claude remembers 19:33 - Hit pound and Claude remembers it forever the whole memory layer is files → drop a CLAUDE.md in your repo and Claude reads it in on every session → nest more in subfolders and it pulls them only where it works → stack them personal to project to enterprise so one config serves the whole team → hit the pound key mid task and Claude writes the new fact into memory itself the best coding agent's memory was never a database, it was markdown you can open and edit

NO1ennn

13,015 görüntüleme • 2 ay önce

It is becoming f*cking dangerous: when Anthropic shipped CLAUDE.md, they didn't even realize they were opening a Pandora's box 1. Make an empty folder on your computer, call it SECOND BRAIN 2. Open Claude Code inside it 3. Add subfolders: MEMORY/, notes/, people/, projects/ 4. Drop a CLAUDE.md in the root with one rule: save every key fact to MEMORY, one topic per file 5. Add @-links in CLAUDE.md so key files load into context every session 6. Run claude mcp add to connect Playwright MCP, now Claude controls a real browser 7. Build a skill in .claude/skills/ so one command runs the entire routine Folder setup: about 5 minutes Your Claude becomes a second brain: it keeps every key fact between sessions, loads your main files by itself and drives a real browser Full guide in the article below
0:43

It is becoming f*cking dangerous: when Anthropic shipped CLAUDE.md, they didn't even realize they were opening a Pandora's box 1. Make an empty folder on your computer, call it SECOND BRAIN 2. Open Claude Code inside it 3. Add subfolders: MEMORY/, notes/, people/, projects/ 4. Drop a CLAUDE.md in the root with one rule: save every key fact to MEMORY, one topic per file 5. Add @-links in CLAUDE.md so key files load into context every session 6. Run claude mcp add to connect Playwright MCP, now Claude controls a real browser 7. Build a skill in .claude/skills/ so one command runs the entire routine Folder setup: about 5 minutes Your Claude becomes a second brain: it keeps every key fact between sessions, loads your main files by itself and drives a real browser Full guide in the article below

plusxbt

68,971 görüntüleme • 9 gün önce

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.
0:22

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

Daniel San

57,445 görüntüleme • 8 ay önce

Built a 3-layer Memory Stack that wires Claude Code directly into Obsidian with skills so it never forgets your project DNA, your notes, your standards, or your goals. Fixes most of Claude Code's memory issues.
1:38

Built a 3-layer Memory Stack that wires Claude Code directly into Obsidian with skills so it never forgets your project DNA, your notes, your standards, or your goals. Fixes most of Claude Code's memory issues.

WorldofAI

115,934 görüntüleme • 7 ay önce

THIS IS A PURE F*CKING GOLDMINE Anthropic just documented how Claude Code actually remembers your entire codebase across sessions The idea: stop dumping 500 lines into CLAUDE.md and let the new Haiku 5.5 manage an auto-indexing memory tree while Opus 5.5 audits your rules Zero context bloat, path-scoped rules that load only when touched, and persistent learnings that survive across Git worktrees You just inspect and steer it in Claude Code like this: /memory here's how it splits: → Opus 5.5 audits rule integrity: runs /doctor prompt-audit to catch stale instructions, contradictions, and obsolete configs → Sonnet 5.5 writes path-scoped rules: drafts modular .claude/rules/ with frontmatter globs so rules only load for matching files → Haiku 5.5 maintains auto-memory: indexes MEMORY.md at 340 tok/s and moves deep context into dedicated topic files → you stop burning Opus context on static prompt bloat and keep your session token count razor thin The memory architecture runs through three proven layers: → path-scoped rules: .claude/rules/*.md load strictly when touched files match, saving up to 80% context → auto-memory index: MEMORY.md caps at 200 lines / 25KB, keeping startup instant across all worktrees → on-demand topic files: detailed architectural notes live in ~/.claude/projects/ /memory/ without polluting the turn Opus audits, Haiku indexes, Sonnet scopes JEV engineering is the same move one layer down: the forks that need no thinker (which rule file matches, auto-memory threshold, stale timestamp drop) resolve in code in 15ms, and the frontier models only see the ones that split the full tree > Opus 5.5 audits rule consistency and runs /doctor prompt-audit > Haiku 5.5 prunes and indexes MEMORY.md at 340 tok/s (1M context) > Sonnet 5.5 writes path-scoped rules in .claude/rules/ with frontmatter globs > on-demand topic files capture edge cases without burning startup tokens > ~/.claude/projects/ /memory/ shares learnings across all Git worktrees > Opus 5.5 enforces zero-regression architecture standards > JEV routes path matching in milliseconds paste the tree and this prompt into Claude Code ↓ "Reconfigure my Claude Code memory architecture around this tree: 1. Audit my current CLAUDE.md with /doctor prompt-audit and strip out stale or conflicting rules 2. Move file-specific instructions into .claude/rules/*.md with YAML frontmatter path globs 3. Delegate MEMORY.md indexing and 200-line threshold management to Haiku 5.5 at 340 tok/s 4. Route detailed architectural learnings and edge cases to Sonnet 5.5 as on-demand topic files 5. Reserve Opus 5.5 for high-level rule consistency and cross-worktree memory verification Show me every change as a diff first. No edits until I say go" ↳ save it, it'll come in handy
0:25

THIS IS A PURE F*CKING GOLDMINE Anthropic just documented how Claude Code actually remembers your entire codebase across sessions The idea: stop dumping 500 lines into CLAUDE.md and let the new Haiku 5.5 manage an auto-indexing memory tree while Opus 5.5 audits your rules Zero context bloat, path-scoped rules that load only when touched, and persistent learnings that survive across Git worktrees You just inspect and steer it in Claude Code like this: /memory here's how it splits: → Opus 5.5 audits rule integrity: runs /doctor prompt-audit to catch stale instructions, contradictions, and obsolete configs → Sonnet 5.5 writes path-scoped rules: drafts modular .claude/rules/ with frontmatter globs so rules only load for matching files → Haiku 5.5 maintains auto-memory: indexes MEMORY.md at 340 tok/s and moves deep context into dedicated topic files → you stop burning Opus context on static prompt bloat and keep your session token count razor thin The memory architecture runs through three proven layers: → path-scoped rules: .claude/rules/*.md load strictly when touched files match, saving up to 80% context → auto-memory index: MEMORY.md caps at 200 lines / 25KB, keeping startup instant across all worktrees → on-demand topic files: detailed architectural notes live in ~/.claude/projects/ /memory/ without polluting the turn Opus audits, Haiku indexes, Sonnet scopes JEV engineering is the same move one layer down: the forks that need no thinker (which rule file matches, auto-memory threshold, stale timestamp drop) resolve in code in 15ms, and the frontier models only see the ones that split the full tree > Opus 5.5 audits rule consistency and runs /doctor prompt-audit > Haiku 5.5 prunes and indexes MEMORY.md at 340 tok/s (1M context) > Sonnet 5.5 writes path-scoped rules in .claude/rules/ with frontmatter globs > on-demand topic files capture edge cases without burning startup tokens > ~/.claude/projects/ /memory/ shares learnings across all Git worktrees > Opus 5.5 enforces zero-regression architecture standards > JEV routes path matching in milliseconds paste the tree and this prompt into Claude Code ↓ "Reconfigure my Claude Code memory architecture around this tree: 1. Audit my current CLAUDE.md with /doctor prompt-audit and strip out stale or conflicting rules 2. Move file-specific instructions into .claude/rules/*.md with YAML frontmatter path globs 3. Delegate MEMORY.md indexing and 200-line threshold management to Haiku 5.5 at 340 tok/s 4. Route detailed architectural learnings and edge cases to Sonnet 5.5 as on-demand topic files 5. Reserve Opus 5.5 for high-level rule consistency and cross-worktree memory verification Show me every change as a diff first. No edits until I say go" ↳ save it, it'll come in handy

mirku

30,878 görüntüleme • 12 gün önce

Most people use Claude Code like a chatbot. That is the mistake. The real power is not in better prompts. It is in controlling the session. /init gives the project memory. /memory keeps the rules alive. /clear removes old context. /compact saves the important parts. /context shows where the tokens go. /rewind fixes bad turns. /plan stops blind edits. /model matches the brain to the task. Claude Code gets 10x better when you stop letting the conversation run itself. Memory. Context. Boundaries. Rollback. Planning. That is the system. Stop asking Claude Code to “just do it”. Teach it what to remember, what to forget, and when to stop. Bookmark this before your next session turns into 40 messages of context debt.
0:33

Most people use Claude Code like a chatbot. That is the mistake. The real power is not in better prompts. It is in controlling the session. /init gives the project memory. /memory keeps the rules alive. /clear removes old context. /compact saves the important parts. /context shows where the tokens go. /rewind fixes bad turns. /plan stops blind edits. /model matches the brain to the task. Claude Code gets 10x better when you stop letting the conversation run itself. Memory. Context. Boundaries. Rollback. Planning. That is the system. Stop asking Claude Code to “just do it”. Teach it what to remember, what to forget, and when to stop. Bookmark this before your next session turns into 40 messages of context debt.

Fluixo

19,226 görüntüleme • 4 ay önce

It is becoming f*cking dangerous: when Anthropic released this, they didn't even realize what a massive chain reaction they were triggering 1. Set up a folder called SECOND BRAIN on your computer 2. Open Claude Code inside that directory and configure GWS CLI to give it access to your Google Drive, Gmail, and Calendar 3. Make a new subfolder named MEMORY inside the root folder 4. Add a ⁠CLAUDE.md⁠ file to the root directory with instructions to log all critical context inside the MEMORY folder 5. Download and install Graphify from GitHub to connect your notes into a knowledge graph 6. Set up Playwright so Claude gets full browser automation capabilities 7. Configure skills to bundle everything into a single automation executable triggered by one command The entire setup takes roughly 20 minutes Transform Claude into your ultimate second brain, packed with persistent memory, direct cloud and email integration, plus seamless web browsing Check out the full step-by-step guide in the article below
0:22

It is becoming f*cking dangerous: when Anthropic released this, they didn't even realize what a massive chain reaction they were triggering 1. Set up a folder called SECOND BRAIN on your computer 2. Open Claude Code inside that directory and configure GWS CLI to give it access to your Google Drive, Gmail, and Calendar 3. Make a new subfolder named MEMORY inside the root folder 4. Add a ⁠CLAUDE.md⁠ file to the root directory with instructions to log all critical context inside the MEMORY folder 5. Download and install Graphify from GitHub to connect your notes into a knowledge graph 6. Set up Playwright so Claude gets full browser automation capabilities 7. Configure skills to bundle everything into a single automation executable triggered by one command The entire setup takes roughly 20 minutes Transform Claude into your ultimate second brain, packed with persistent memory, direct cloud and email integration, plus seamless web browsing Check out the full step-by-step guide in the article below

Bober_smart

244,345 görüntüleme • 14 gün önce

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇
20:54

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

YanXbt

23,088 görüntüleme • 3 ay önce

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.
0:50

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,359 görüntüleme • 2 ay önce

Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem! It’s called Claude-Mem, and it lets Claude remember your work across sessions. ⚡ Slash token usage by up to 95% every time you start a session. 🔧 Unlock the ability to make 20× more tool calls before hitting limits. My Video:
0:58

Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem! It’s called Claude-Mem, and it lets Claude remember your work across sessions. ⚡ Slash token usage by up to 95% every time you start a session. 🔧 Unlock the ability to make 20× more tool calls before hitting limits. My Video:

WorldofAI

28,687 görüntüleme • 9 ay önce

I can't f*cking believe people still use Claude Code like a chat bot. the ones getting real work out of it gave it a folder, tools and a map of everything it knows. that map is graphify. you point it at a folder and it builds a knowledge graph of everything inside. code, docs, PDFs, images, even video. then your agent searches the graph instead of opening files one by one. 134k stars on GitHub. backed by Y Combinator. and most people still haven't tried it. what makes it different: -> the code part runs fully on your machine. nothing gets uploaded -> no vector database, no embeddings. it's a real graph your agent walks through -> every connection is labeled: read straight from the source, or figured out by graphify. you always know which -> you get 3 files: a clickable map you open in your browser, a report with the key ideas and surprising links, and the full graph your agent queries anytime on a public memory benchmark it found the right answer in its top 10 results about half the time. mem0 managed under 5%. and building the graph cost 0 model credits. here's the full setup around it: > create a folder called AGENT, open Claude Code inside it, and add a CLAUDE.md with one line: "save every decision, person and project to memory/" > run uv tool install graphifyy, then graphify install, then type /graphify . and the whole folder becomes a map Claude can search in seconds > run npm install -g @googleworkspace/cli, then gws auth setup and gws auth login. Claude can now read your Gmail, Calendar and Drive > run claude mcp add playwright npx @playwright/mcp@latest. it gets its own browser for sites, forms and data > run npm i -g jev-guard, then jev-guard key "...", then jev-guard install claude. Jev checks every action before it runs. rm -rf gets blocked, emails with hidden instructions get flagged, about 4 cents per 1,000 checks > type /schedule and say "every morning at 7:30, send me what came in, what you drafted and what needs my yes" one folder. 15 minutes. and Claude finally knows where everything is. all the repos: -> -> -> -> bookmark this and try graphify first. it's the piece everyone skips.
0:21

I can't f*cking believe people still use Claude Code like a chat bot. the ones getting real work out of it gave it a folder, tools and a map of everything it knows. that map is graphify. you point it at a folder and it builds a knowledge graph of everything inside. code, docs, PDFs, images, even video. then your agent searches the graph instead of opening files one by one. 134k stars on GitHub. backed by Y Combinator. and most people still haven't tried it. what makes it different: -> the code part runs fully on your machine. nothing gets uploaded -> no vector database, no embeddings. it's a real graph your agent walks through -> every connection is labeled: read straight from the source, or figured out by graphify. you always know which -> you get 3 files: a clickable map you open in your browser, a report with the key ideas and surprising links, and the full graph your agent queries anytime on a public memory benchmark it found the right answer in its top 10 results about half the time. mem0 managed under 5%. and building the graph cost 0 model credits. here's the full setup around it: > create a folder called AGENT, open Claude Code inside it, and add a CLAUDE.md with one line: "save every decision, person and project to memory/" > run uv tool install graphifyy, then graphify install, then type /graphify . and the whole folder becomes a map Claude can search in seconds > run npm install -g @googleworkspace/cli, then gws auth setup and gws auth login. Claude can now read your Gmail, Calendar and Drive > run claude mcp add playwright npx @playwright/mcp@latest. it gets its own browser for sites, forms and data > run npm i -g jev-guard, then jev-guard key "...", then jev-guard install claude. Jev checks every action before it runs. rm -rf gets blocked, emails with hidden instructions get flagged, about 4 cents per 1,000 checks > type /schedule and say "every morning at 7:30, send me what came in, what you drafted and what needs my yes" one folder. 15 minutes. and Claude finally knows where everything is. all the repos: -> -> -> -> bookmark this and try graphify first. it's the piece everyone skips.

kiosa

29,075 görüntüleme • 8 gün önce

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
13:23

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,568 görüntüleme • 4 ay önce

you wrote a long careful note so it would remember the thing properly the note is the part that does not load what loads at the start of every session is the index line, the one sentence summary, and only the first two hundred lines of it or the first twenty five kilobytes, whichever runs out first the topic files underneath are read on demand, which means only if something in that one line makes it go and open them i checked mine: thirty nine index lines loading every session, thirty nine topic files loading none of the time the detail you were careful about is not context it is a footnote your agent has to choose to open and the sentence you dashed off in three seconds is the whole memory
0:45

you wrote a long careful note so it would remember the thing properly the note is the part that does not load what loads at the start of every session is the index line, the one sentence summary, and only the first two hundred lines of it or the first twenty five kilobytes, whichever runs out first the topic files underneath are read on demand, which means only if something in that one line makes it go and open them i checked mine: thirty nine index lines loading every session, thirty nine topic files loading none of the time the detail you were careful about is not context it is a footnote your agent has to choose to open and the sentence you dashed off in three seconds is the whole memory

Lagosaur

28,369 görüntüleme • 1 ay önce

The engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work. I've seen $300 courses that don't cover what he shows in the first 10 minutes. CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns. Watch it, then read the guide below on the Claude features 99% of users never find.
27:17

The engineer who built Claude Code just dropped a 28-minute video on how to write prompts that actually work. I've seen $300 courses that don't cover what he shows in the first 10 minutes. CLAUDE.md files, memory shortcuts, parallel sessions, prompting patterns. Watch it, then read the guide below on the Claude features 99% of users never find.

Anatoli Kopadze

759,191 görüntüleme • 3 ay önce

ANTHROPIC JUST KILLED A DOZEN AI MEMORY STARTUPS WITH ONE BUILT IN TOOL people raised millions to give AI long term memory. Anthropic just shipped it straight into the API. free, no framework, no vector database. it is a folder of files Claude reads and writes. that is it. the trick: it is client side. Claude never touches your data directly. it only requests a file operation, and your app runs it against storage you control. before every task Claude checks its /memories directory, pulls back what it learned last time, and keeps going. when it learns something new, it writes it down. six plain operations do all of it: > view, create, str_replace, insert, delete, rename why it actually wins: > just in time retrieval, it reads on demand instead of loading everything up front > it survives a context reset, so a long job resumes instead of starting over > every path is locked to /memories, so it cannot wander your filesystem > works on every Claude 4 model, no beta flag, generally available Anthropic even pairs it with context editing and compaction, so the live window stays tiny while the important stuff lives on disk. the whole thing in one line: Claude keeps the notes, you keep the storage. bookmark this before you pay for a memory layer you no longer need.
0:26

ANTHROPIC JUST KILLED A DOZEN AI MEMORY STARTUPS WITH ONE BUILT IN TOOL people raised millions to give AI long term memory. Anthropic just shipped it straight into the API. free, no framework, no vector database. it is a folder of files Claude reads and writes. that is it. the trick: it is client side. Claude never touches your data directly. it only requests a file operation, and your app runs it against storage you control. before every task Claude checks its /memories directory, pulls back what it learned last time, and keeps going. when it learns something new, it writes it down. six plain operations do all of it: > view, create, str_replace, insert, delete, rename why it actually wins: > just in time retrieval, it reads on demand instead of loading everything up front > it survives a context reset, so a long job resumes instead of starting over > every path is locked to /memories, so it cannot wander your filesystem > works on every Claude 4 model, no beta flag, generally available Anthropic even pairs it with context editing and compaction, so the live window stays tiny while the important stuff lives on disk. the whole thing in one line: Claude keeps the notes, you keep the storage. bookmark this before you pay for a memory layer you no longer need.

NO1ennn

43,756 görüntüleme • 2 ay önce

Claude Tag is the next evolution of agents. It's a proactive, multiplayer agent with memory and identity, built on top of Claude Code. Learn more about how Claude Tag works and best practices for using it in this deep dive.
6:33

Claude Tag is the next evolution of agents. It's a proactive, multiplayer agent with memory and identity, built on top of Claude Code. Learn more about how Claude Tag works and best practices for using it in this deep dive.

ClaudeDevs

454,269 görüntüleme • 4 ay önce

HOLY F*CKING SH*T you waste 20 minutes every morning re-explaining your project to Claude like it has never heard of you, and Karpathy showed you the fix a year ago Andrej Karpathy, co-founder of OpenAI, posted one idea that pulled 16 million views: stop using AI to write code. use it to build a second brain You point Claude Code to a folder, drop any source in there (article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. it compounds like interest: the more you feed it, the sharper every answer gets Here is the gist: → install Obsidian, create a vault, open it in Claude Code → paste Karpathy's wiki prompt and tell Claude to build it → Claude creates 3 folders: raw for sources, wiki for its pages, CLAUDE.md that runs everything → drop any source into raw and say "ingest this" → ask anything across all of it, forever 5 minutes to set up. and you never open a blank Claude chat again the full step-by-step is in the article below bookmark this before you waste another monday
0:21

HOLY F*CKING SH*T you waste 20 minutes every morning re-explaining your project to Claude like it has never heard of you, and Karpathy showed you the fix a year ago Andrej Karpathy, co-founder of OpenAI, posted one idea that pulled 16 million views: stop using AI to write code. use it to build a second brain You point Claude Code to a folder, drop any source in there (article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. it compounds like interest: the more you feed it, the sharper every answer gets Here is the gist: → install Obsidian, create a vault, open it in Claude Code → paste Karpathy's wiki prompt and tell Claude to build it → Claude creates 3 folders: raw for sources, wiki for its pages, CLAUDE.md that runs everything → drop any source into raw and say "ingest this" → ask anything across all of it, forever 5 minutes to set up. and you never open a blank Claude chat again the full step-by-step is in the article below bookmark this before you waste another monday

mojesko

95,829 görüntüleme • 3 gün önce

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.
4:19

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.

DegenCalls

20,124 görüntüleme • 4 ay önce

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.
10:19

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 görüntüleme • 4 ay önce

STOP TREATING YOUR OBSIDIAN VAULT AS AI MEMORY. WITHOUT ONE ROUTING FILE, CLAUDE IS BURNING TOKENS ON NOISE YOU FORGOT YOU SAVED. a folder full of markdown does not make Claude smart. what makes it smart is what your agent can find without rereading the whole vault. storage: write → tag → graph → agent scans everything the model touches the entire vault, hallucinates half of it, forgets by the next session. memory: write → index → route → agent opens two files the model reads the index first, follows one path, answers with a link back to the note. i have rebuilt my Obsidian vault three times this year. the first two collapsed inside a month because Claude kept guessing which folder to open. the third one holds because the first thing i ship now is the routing file. Markdown, links, and the graph view are not what changed. what changed was a one-line description per folder, written for the agent, kept current, loaded before any retrieval call. if your agent still starts every session by scanning your whole vault, you are running storage. write the routing file today.
0:20

STOP TREATING YOUR OBSIDIAN VAULT AS AI MEMORY. WITHOUT ONE ROUTING FILE, CLAUDE IS BURNING TOKENS ON NOISE YOU FORGOT YOU SAVED. a folder full of markdown does not make Claude smart. what makes it smart is what your agent can find without rereading the whole vault. storage: write → tag → graph → agent scans everything the model touches the entire vault, hallucinates half of it, forgets by the next session. memory: write → index → route → agent opens two files the model reads the index first, follows one path, answers with a link back to the note. i have rebuilt my Obsidian vault three times this year. the first two collapsed inside a month because Claude kept guessing which folder to open. the third one holds because the first thing i ship now is the routing file. Markdown, links, and the graph view are not what changed. what changed was a one-line description per folder, written for the agent, kept current, loaded before any retrieval call. if your agent still starts every session by scanning your whole vault, you are running storage. write the routing file today.

kocer

17,240 görüntüleme • 2 ay önce