ๆญฃๅœจๅŠ ่ฝฝ่ง†้ข‘...

่ง†้ข‘ๅŠ ่ฝฝๅคฑ่ดฅ

่ฟ”ๅ›ž้ฆ–้กต

Usual memory systems learn the moment data flows in, then never look at it again. supermemory ๐š๐š›๐šŽ๐šŠ๐š–๐šœ. Status done means the chunks are indexed. Memories come from a second phase, where related documents are grouped so memories form from coherent units, never one isolated write. When you go quiet,...

supermemory's profile picture

supermemory

29,089 subscribers

11,021 ๆฌก่ง‚็œ‹ โ€ข 12 ๅคฉๅ‰ โ€ขvia X (Twitter)

LIVE

Anya Rossiโ€ข Live Now

Private livecam show

0 ๆก่ฏ„่ฎบ

ๆš‚ๆ— ่ฏ„่ฎบ

ๅŽŸๅง‹ๅธ–ๅญ็š„่ฏ„่ฎบๅฐ†ๆ˜พ็คบๅœจ่ฟ™้‡Œ

็›ธๅ…ณ่ง†้ข‘

Introducing Memory 101 ๐Ÿง  Every ai tool you use is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: โ†’ rag vs memory, why most "ai memory" is just search โ†’ the 5 things real memory needs: facts, profiles, graphs, time, forgetting โ†’ why good memory has to forget things follow along!
0:50

Introducing Memory 101 ๐Ÿง  Every ai tool you use is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: โ†’ rag vs memory, why most "ai memory" is just search โ†’ the 5 things real memory needs: facts, profiles, graphs, time, forgetting โ†’ why good memory has to forget things follow along!

supermemory

17,235 ๆฌก่ง‚็œ‹ โ€ข 3 ไธชๆœˆๅ‰

Agents need continual learning. At supermemory, we are doubling down and pushing the frontier for memory and in-context learning, for every agent and use case. Introducing ๐š•๐šŽ๐šŠ๐š›๐š—๐šŽ๐š›-๐Ÿท
1:19

Agents need continual learning. At supermemory, we are doubling down and pushing the frontier for memory and in-context learning, for every agent and use case. Introducing ๐š•๐šŽ๐šŠ๐š›๐š—๐šŽ๐š›-๐Ÿท

supermemory

1,867,172 ๆฌก่ง‚็œ‹ โ€ข 1 ไธชๆœˆๅ‰

Introducing the supermemory CLI. MCP isn't dead - but CLIs can be more powerful. So we built the most advanced CLI for agents. EVERY single thing you can do on the supermemory platform can be done by Agents. just prompt them. They are first class users. > npx supermemory
0:34

Introducing the supermemory CLI. MCP isn't dead - but CLIs can be more powerful. So we built the most advanced CLI for agents. EVERY single thing you can do on the supermemory platform can be done by Agents. just prompt them. They are first class users. > npx supermemory

supermemory

96,291 ๆฌก่ง‚็œ‹ โ€ข 6 ไธชๆœˆๅ‰

As soon as you try to run 2 agents on the same data, everything breaks: โ€ข Agent 1 reads some data and starts working. โ€ข Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: โ€ข It sets one shared place where multiple agents will work from โ€ข Each agent gets their own copy of the data โ€ข Agents write their changes to their copy โ€ข When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:
6:51

As soon as you try to run 2 agents on the same data, everything breaks: โ€ข Agent 1 reads some data and starts working. โ€ข Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: โ€ข It sets one shared place where multiple agents will work from โ€ข Each agent gets their own copy of the data โ€ข Agents write their changes to their copy โ€ข When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

Santiago

28,865 ๆฌก่ง‚็œ‹ โ€ข 16 ๅคฉๅ‰

STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they get
0:29

STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they get

NO1ennn

32,454 ๆฌก่ง‚็œ‹ โ€ข 2 ไธชๆœˆๅ‰

The American Dream does not need a makeover. It has worked for 250 years because America is still the place where, no matter who you are or where you came from, you have a real shot at building a better life. Every administration has a responsibility to protect that opportunity. The American Dream is not just part of this country, it is one of its greatest exports.
1:03

The American Dream does not need a makeover. It has worked for 250 years because America is still the place where, no matter who you are or where you came from, you have a real shot at building a better life. Every administration has a responsibility to protect that opportunity. The American Dream is not just part of this country, it is one of its greatest exports.

Kevin O'Leary aka Mr. Wonderful

30,234 ๆฌก่ง‚็œ‹ โ€ข 1 ไธชๆœˆๅ‰

Introducing the new supermemory console. Revamped design, better UX, new functionalities!
0:27

Introducing the new supermemory console. Revamped design, better UX, new functionalities!

supermemory

46,568 ๆฌก่ง‚็œ‹ โ€ข 25 ๅคฉๅ‰

Introducing the supermemory company brain A super employee with all the knowledge of your company, lives in your slack. - Learns how the team works and talks - Works across all your tools - Permission controls built-in for big teams 2 mins to set up. just invite to slack ๐Ÿ‘‡
1:12

Introducing the supermemory company brain A super employee with all the knowledge of your company, lives in your slack. - Learns how the team works and talks - Works across all your tools - Permission controls built-in for big teams 2 mins to set up. just invite to slack ๐Ÿ‘‡

supermemory

621,682 ๆฌก่ง‚็œ‹ โ€ข 2 ไธชๆœˆๅ‰

IF YOU ARE STILL PROMPTING BY HAND IN 2026 YOU ARE ALREADY BEHIND the guy who built Claude Code at Anthropic said he stopped writing prompts a long time ago he runs loops instead, systems that write the prompts for him and keep working while he sleeps you type one request at a time and wait for the answer he built a machine that fires hundreds of requests, checks its own output and fixes itself with no hands on the keyboard here is how you make the jump step 1 stop treating the model like a chat box and start treating it like a worker you manage step 2 write a loop that takes a goal, breaks it into tasks and sends each one to the model on its own step 3 add a second pass where the model grades its own work and retries whatever failed step 4 let it run on a timer so it keeps shipping while you are away from the screen the person still typing prompts one by one is doing the work of a intern the person running loops is doing the work of a whole team off one laptop prompting was never the skill, building the system that prompts for you is
0:56

IF YOU ARE STILL PROMPTING BY HAND IN 2026 YOU ARE ALREADY BEHIND the guy who built Claude Code at Anthropic said he stopped writing prompts a long time ago he runs loops instead, systems that write the prompts for him and keep working while he sleeps you type one request at a time and wait for the answer he built a machine that fires hundreds of requests, checks its own output and fixes itself with no hands on the keyboard here is how you make the jump step 1 stop treating the model like a chat box and start treating it like a worker you manage step 2 write a loop that takes a goal, breaks it into tasks and sends each one to the model on its own step 3 add a second pass where the model grades its own work and retries whatever failed step 4 let it run on a timer so it keeps shipping while you are away from the screen the person still typing prompts one by one is doing the work of a intern the person running loops is doing the work of a whole team off one laptop prompting was never the skill, building the system that prompts for you is

Atlas

16,641 ๆฌก่ง‚็œ‹ โ€ข 1 ไธชๆœˆๅ‰

Everyone wants AI with a bigger memory, we think that's the wrong goal.. We sat down with Soham Daga, AI researcher at supermemory, for a deep dive into how machines actually remember you. what we get into: - why every AI forgets you - why forgetting is the hard part, not remembering - memory that understands time - going past reranking with aggregation - buckets for agent memory - SMFS, cutting tokens by 60% A conversation on the future of context, full episode below!
19:42

Everyone wants AI with a bigger memory, we think that's the wrong goal.. We sat down with Soham Daga, AI researcher at supermemory, for a deep dive into how machines actually remember you. what we get into: - why every AI forgets you - why forgetting is the hard part, not remembering - memory that understands time - going past reranking with aggregation - buckets for agent memory - SMFS, cutting tokens by 60% A conversation on the future of context, full episode below!

supermemory

20,600 ๆฌก่ง‚็œ‹ โ€ข 1 ไธชๆœˆๅ‰

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below
0:40

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

Hanako

19,160 ๆฌก่ง‚็œ‹ โ€ข 2 ไธชๆœˆๅ‰

Meet the minds building supermemory! ๐Ÿง  Our research engineer Prasanna breaks down how memory actually works, in your head, and in AI. inside the episode: โ†’ why forgetting is a feature and not a failure โ†’ what your hippocampus inside brain is really doing all day โ†’ how "dreaming" turns a messy day into long-term memory โ†’ what the AI labs get right, and wrong, when they copy the brain โ†’ why a bigger context window still isn't memory the line worth sitting with: stop asking where memory is stored. start asking what gets kept, and why the brain chose it. full episode below โ†“
19:55

Meet the minds building supermemory! ๐Ÿง  Our research engineer Prasanna breaks down how memory actually works, in your head, and in AI. inside the episode: โ†’ why forgetting is a feature and not a failure โ†’ what your hippocampus inside brain is really doing all day โ†’ how "dreaming" turns a messy day into long-term memory โ†’ what the AI labs get right, and wrong, when they copy the brain โ†’ why a bigger context window still isn't memory the line worth sitting with: stop asking where memory is stored. start asking what gets kept, and why the brain chose it. full episode below โ†“

supermemory

14,752 ๆฌก่ง‚็œ‹ โ€ข 2 ไธชๆœˆๅ‰

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. โ†ณ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result โ†ณ between calls: split, fan out, merge, gate, send back Prompts โ†’ Context โ†’ Harness โ†’ Loops โ†’ Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. โ†ณ it retries, it times out, it logs, it assembles context, it holds one call up beautifully โ†ณ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below โ†“
0:53

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. โ†ณ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result โ†ณ between calls: split, fan out, merge, gate, send back Prompts โ†’ Context โ†’ Harness โ†’ Loops โ†’ Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. โ†ณ it retries, it times out, it logs, it assembles context, it holds one call up beautifully โ†ณ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below โ†“

Hanako

51,536 ๆฌก่ง‚็œ‹ โ€ข 26 ๅคฉๅ‰

Dear Los Angeles, One month ago, DATALAND opened its doors. Today, it has a memory. What stays with me is not the 1.2 billion data points. It is the silence between them. The moment when a room full of strangers becomes completely stillโ€”when the technology disappears and data becomes memory. We did not build DATALAND to show what AI can do. We built it to ask what AI can help us feel, remember, and protect. For years, we have asked: Can a building dream? This month, you answered: only when people dream inside it. You entered with your own memories and curiosity. You slowed down. You listened. You became part of the workโ€”and gave this museum the one thing no model can generate: a shared memory. A museum opens in a day. It comes alive one encounter at a time. One month ago, we opened the doors to our dream. Today, it no longer belongs only to us. With love and gratitude, Refik, Efsun & the DATALAND Team
0:52

Dear Los Angeles, One month ago, DATALAND opened its doors. Today, it has a memory. What stays with me is not the 1.2 billion data points. It is the silence between them. The moment when a room full of strangers becomes completely stillโ€”when the technology disappears and data becomes memory. We did not build DATALAND to show what AI can do. We built it to ask what AI can help us feel, remember, and protect. For years, we have asked: Can a building dream? This month, you answered: only when people dream inside it. You entered with your own memories and curiosity. You slowed down. You listened. You became part of the workโ€”and gave this museum the one thing no model can generate: a shared memory. A museum opens in a day. It comes alive one encounter at a time. One month ago, we opened the doors to our dream. Today, it no longer belongs only to us. With love and gratitude, Refik, Efsun & the DATALAND Team

Refik Anadol

14,137 ๆฌก่ง‚็œ‹ โ€ข 2 ไธชๆœˆๅ‰

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. โ†ณ inside a unit: the loop. produce, check, correct, repeat until green โ†ณ between units: the graph. split, fan out, merge, gate, send back Prompts โ†’ Context โ†’ Harness โ†’ Loops โ†’ Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. โ†ณ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in โ†ณ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below โ†“
0:41

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. โ†ณ inside a unit: the loop. produce, check, correct, repeat until green โ†ณ between units: the graph. split, fan out, merge, gate, send back Prompts โ†’ Context โ†’ Harness โ†’ Loops โ†’ Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. โ†ณ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in โ†ณ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below โ†“

Hanako

73,867 ๆฌก่ง‚็œ‹ โ€ข 1 ไธชๆœˆๅ‰

Episode 1 of memory 101: the anatomy of memory.๐Ÿง  real memory isn't one thing. it's five: > facts > profiles > relationships > temporality > forgetting "AI memory" products build one of these primitives and stop. Real memory is when all five compose. Next, stay tuned for RAG โ†“
1:26

Episode 1 of memory 101: the anatomy of memory.๐Ÿง  real memory isn't one thing. it's five: > facts > profiles > relationships > temporality > forgetting "AI memory" products build one of these primitives and stop. Real memory is when all five compose. Next, stay tuned for RAG โ†“

supermemory

23,127 ๆฌก่ง‚็œ‹ โ€ข 3 ไธชๆœˆๅ‰

โ€œYou take a a Rolex Daytona today, and you look back at the first one in 1963. To me, the one in 1963 is better aesthetically. Because its the purest form of the concept. The purest form of the idea. Everything else from there has been layered on because they need to sell more and they need to come up with a new version and new model. This is what happens. They couldn't still sell that first version. That's not the way the world works. You have to keep making new stuff. The pure idea, the pure form of the thing [is best]โ€
0:35

โ€œYou take a a Rolex Daytona today, and you look back at the first one in 1963. To me, the one in 1963 is better aesthetically. Because its the purest form of the concept. The purest form of the idea. Everything else from there has been layered on because they need to sell more and they need to come up with a new version and new model. This is what happens. They couldn't still sell that first version. That's not the way the world works. You have to keep making new stuff. The pure idea, the pure form of the thing [is best]โ€

David Senra

105,087 ๆฌก่ง‚็œ‹ โ€ข 7 ไธชๆœˆๅ‰

Paul Verhoeven on whether the events that take place in "Total Recall" (1990) are real or just a dream: "It is both. To be honest, thatโ€™s what I want. I made the movie in a way that it would be true on both levels, and I spent a lot of time to get that. If you want a scientific explanation, you know, of course, in quantum mechanics there is a very interesting principle, the principle of uncertainty, Heisenbergโ€™s principle. If you have a big object and if you try to measure the place of the object and the velocity of the object at the same time, the more precisely you measure velocity the less precise place gets. So thatโ€™s the principle. That means, of course, that there are different realities possible at the same moment. What I wanted to do in 'Total Recall' is to do a movie where both levels are true. I mean for me, of course, the film anyhow has to do with two realities, one being the reality of going as a secret agent to Mars and discovering that there is a problem, and solving the problem, which is starting the nuclear reactor and helping the guerrillas and destroying Cohaagen. The second level of the movie, of course, is that from the moment that he goes into the Rekall chair โ€˜til the end itโ€™s a dream, and I tried to make that second level work throughout the whole movie. So thereโ€™s the dream level which starts when he gets into the chair and the thing is in his neck, and that would go throughout the whole movie, so in the next scene where they say, Oh, thereโ€™s a problem, thereโ€™s a big glitch here, that would be already the dream, of course. Thatโ€™s where the dream starts. And the next scene where they are fighting and stuff would be part of his dream, convincing him that it is real, because there is a glitch but that would be part of the program. It would be built into the program to make him accept the fact that itโ€™s real, but itโ€™s a dream. If you look at the movie, if you havenโ€™t seen it, or for the second time, youโ€™ll see that the whole program thatโ€™s set up at the beginning when he goes to the Rekall office and he talks to this guy who sells him the program on Mars, youโ€™ll see that he gets everything that he wants: he gets the trip to Mars, he gets the girl, the exotic girl, he ki!!s the bad guys, and he saves the entire planet. Thatโ€™s what he does. And thatโ€™s basically the dream. Even halfway through the movie, you may remember, this other guy comes in, Dr. Edgemar, and tells him that heโ€™s in a dream, that heโ€™s still in the Rekall chair, and then Arnold says, โ€œIf Iโ€™m there, I can ki!! you.โ€ And he puts a gun to his head and the guy says, โ€œSure, no problem for me, big problem for you, because you will be psychotic from now on because the walls of reality will fall apart. One moment you will be the savior of the rebel cause, the next moment youโ€™ll be Cohaagenโ€™s bosom buddy, but in the endโ€”you will even have these strange fantasies about alien civilizationsโ€”but at the end you will be lobotomized.โ€ And then if you see the movie, you realize that all these things happen. I mean he is lobotomized at the end. Thatโ€™s why at the last shot, when they are so happy and kissing each other, it slowly fades to white, which for me meant, โ€œOK, there he goes. Thatโ€™s the end-thatโ€™s the dreamโ€”they lobotomized him.โ€ And all the other things happened, he finds the alien civilization, he rescues the planet, he finds the good girl, he k!!ls the bad guys, but itโ€™s a dream. Now, of course you can see it as a reality, too. So at the end of the movie, getting to white means either itโ€™s a happy ending or he loses his brains . . . which is probably also a happy ending, I donโ€™t know. That was basically what l wantedโ€”that at the end there would be two possibilities, and they would be both trueโ€”for me they are both trueโ€”itโ€™s not either one or the other. Itโ€™s not that either itโ€™s a dream or it is a reality. It is a dream and it is a reality. And I think theyโ€™re both there." (Paul Verhoeven's interview with Chris Shea & Wade Jennings, 1992) P.S: On this day, 36 years ago, "Total Recall" (1990) premiered in Los Angeles, California, USA.
1:25

Paul Verhoeven on whether the events that take place in "Total Recall" (1990) are real or just a dream: "It is both. To be honest, thatโ€™s what I want. I made the movie in a way that it would be true on both levels, and I spent a lot of time to get that. If you want a scientific explanation, you know, of course, in quantum mechanics there is a very interesting principle, the principle of uncertainty, Heisenbergโ€™s principle. If you have a big object and if you try to measure the place of the object and the velocity of the object at the same time, the more precisely you measure velocity the less precise place gets. So thatโ€™s the principle. That means, of course, that there are different realities possible at the same moment. What I wanted to do in 'Total Recall' is to do a movie where both levels are true. I mean for me, of course, the film anyhow has to do with two realities, one being the reality of going as a secret agent to Mars and discovering that there is a problem, and solving the problem, which is starting the nuclear reactor and helping the guerrillas and destroying Cohaagen. The second level of the movie, of course, is that from the moment that he goes into the Rekall chair โ€˜til the end itโ€™s a dream, and I tried to make that second level work throughout the whole movie. So thereโ€™s the dream level which starts when he gets into the chair and the thing is in his neck, and that would go throughout the whole movie, so in the next scene where they say, Oh, thereโ€™s a problem, thereโ€™s a big glitch here, that would be already the dream, of course. Thatโ€™s where the dream starts. And the next scene where they are fighting and stuff would be part of his dream, convincing him that it is real, because there is a glitch but that would be part of the program. It would be built into the program to make him accept the fact that itโ€™s real, but itโ€™s a dream. If you look at the movie, if you havenโ€™t seen it, or for the second time, youโ€™ll see that the whole program thatโ€™s set up at the beginning when he goes to the Rekall office and he talks to this guy who sells him the program on Mars, youโ€™ll see that he gets everything that he wants: he gets the trip to Mars, he gets the girl, the exotic girl, he ki!!s the bad guys, and he saves the entire planet. Thatโ€™s what he does. And thatโ€™s basically the dream. Even halfway through the movie, you may remember, this other guy comes in, Dr. Edgemar, and tells him that heโ€™s in a dream, that heโ€™s still in the Rekall chair, and then Arnold says, โ€œIf Iโ€™m there, I can ki!! you.โ€ And he puts a gun to his head and the guy says, โ€œSure, no problem for me, big problem for you, because you will be psychotic from now on because the walls of reality will fall apart. One moment you will be the savior of the rebel cause, the next moment youโ€™ll be Cohaagenโ€™s bosom buddy, but in the endโ€”you will even have these strange fantasies about alien civilizationsโ€”but at the end you will be lobotomized.โ€ And then if you see the movie, you realize that all these things happen. I mean he is lobotomized at the end. Thatโ€™s why at the last shot, when they are so happy and kissing each other, it slowly fades to white, which for me meant, โ€œOK, there he goes. Thatโ€™s the end-thatโ€™s the dreamโ€”they lobotomized him.โ€ And all the other things happened, he finds the alien civilization, he rescues the planet, he finds the good girl, he k!!ls the bad guys, but itโ€™s a dream. Now, of course you can see it as a reality, too. So at the end of the movie, getting to white means either itโ€™s a happy ending or he loses his brains . . . which is probably also a happy ending, I donโ€™t know. That was basically what l wantedโ€”that at the end there would be two possibilities, and they would be both trueโ€”for me they are both trueโ€”itโ€™s not either one or the other. Itโ€™s not that either itโ€™s a dream or it is a reality. It is a dream and it is a reality. And I think theyโ€™re both there." (Paul Verhoeven's interview with Chris Shea & Wade Jennings, 1992) P.S: On this day, 36 years ago, "Total Recall" (1990) premiered in Los Angeles, California, USA.

DepressedBergman

66,465 ๆฌก่ง‚็œ‹ โ€ข 4 ไธชๆœˆๅ‰

Love and Deepspace | New Interactive 5-Star Memoryโฃ๏ธ [Caleb: Clearday Return] ๐ŸชOfficial Discord: The agony of testing the medicine pierces to the bone, yet Caleb rescues you from the depths of suffering. It is said that Master Caleb plays a bone flute and poisons enemies from a thousand miles away. Whether his heart is poison or a cure, only those who keep stirring it can know. "And you never look guilty when you lie to me. Come. Let's go home." โฃ๏ธMortality's Tenderness Event Duration: From 05:00 on Feb. 10 to 04:59 on Feb. 27 (Server Time) You can select three out of the five event-limited 5-Star Memories: [Xavier: Blades with Blossoms], [Zayne: Entwined Kites], [Rafayel: Carved Gemheart], [Sylus: Shared Lanterns], and [Caleb: Clearday Return]. The drop rate of the 3 Memories you selected will be significantly increased. Each time you obtain a 5-Star Memory, there's a 75% chance it will be one of the three Memories you selected. *Notes: 1. During the event, you can change your selected Memories at any time. If you obtain a 5-Star Memory in a wish, there's a 25% chance that it will be an unselected Memory or a permanent 5-Star Memory. 2. After the event ends, the five event-limited Memories will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The Wish event features Precise Wish and a pity system. For more details, please check the in-game rule page. โฃ๏ธLimited Gift: Cumulative Wish Rewards During the event, after making a certain number of Wishes, you can claim various rewards: Universal Headwear [Gilded Trace], Deepspace Wish: Limited*20, His [Memory-Themed Outfits], Limited Gift [Memory-Themed Hairstyle], selectable [Event-Limited 5-Star Memory], and more. *The cumulative rewards are only available during this wish event. โฃ๏ธLimited-Time Memory Growth Bonus During the event, by completing the growth tasks of the event-limited 5-Star Memories, you can claim various Upgrade and Ascension Materials. When the event-limited Memories reach Rank 1, you can claim the [Special-Colored Memory-Themed Outfit] for the corresponding love interest. โ€”โ€”โ€” ๐ŸชDownload and Log in Now: #LoveandDeepspace #MortalitysTenderness #Caleb
1:00

Love and Deepspace | New Interactive 5-Star Memoryโฃ๏ธ [Caleb: Clearday Return] ๐ŸชOfficial Discord: The agony of testing the medicine pierces to the bone, yet Caleb rescues you from the depths of suffering. It is said that Master Caleb plays a bone flute and poisons enemies from a thousand miles away. Whether his heart is poison or a cure, only those who keep stirring it can know. "And you never look guilty when you lie to me. Come. Let's go home." โฃ๏ธMortality's Tenderness Event Duration: From 05:00 on Feb. 10 to 04:59 on Feb. 27 (Server Time) You can select three out of the five event-limited 5-Star Memories: [Xavier: Blades with Blossoms], [Zayne: Entwined Kites], [Rafayel: Carved Gemheart], [Sylus: Shared Lanterns], and [Caleb: Clearday Return]. The drop rate of the 3 Memories you selected will be significantly increased. Each time you obtain a 5-Star Memory, there's a 75% chance it will be one of the three Memories you selected. *Notes: 1. During the event, you can change your selected Memories at any time. If you obtain a 5-Star Memory in a wish, there's a 25% chance that it will be an unselected Memory or a permanent 5-Star Memory. 2. After the event ends, the five event-limited Memories will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The Wish event features Precise Wish and a pity system. For more details, please check the in-game rule page. โฃ๏ธLimited Gift: Cumulative Wish Rewards During the event, after making a certain number of Wishes, you can claim various rewards: Universal Headwear [Gilded Trace], Deepspace Wish: Limited*20, His [Memory-Themed Outfits], Limited Gift [Memory-Themed Hairstyle], selectable [Event-Limited 5-Star Memory], and more. *The cumulative rewards are only available during this wish event. โฃ๏ธLimited-Time Memory Growth Bonus During the event, by completing the growth tasks of the event-limited 5-Star Memories, you can claim various Upgrade and Ascension Materials. When the event-limited Memories reach Rank 1, you can claim the [Special-Colored Memory-Themed Outfit] for the corresponding love interest. โ€”โ€”โ€” ๐ŸชDownload and Log in Now: #LoveandDeepspace #MortalitysTenderness #Caleb

Love and Deepspace

369,932 ๆฌก่ง‚็œ‹ โ€ข 8 ไธชๆœˆๅ‰