screen memory: the complete guide

screen memory is a category of tool that records what is on your display, runs ocr so the pixels become searchable text, and lets you ask questions of it later.

people also call it screen recording ai, personal ai memory, or lifelogging when the scope goes wider than work. the defining property is not the recording. it is that you never decide to save anything. every notes app, wiki and bookmarking system asks you to notice something is worth keeping and act on it, and that filing tax is why almost all of them decay. screen memory has a cost per item of zero, so it catches the 95% of your work you would never have filed.

this is the overview of what a screen memory app does. everything below links to a fuller treatment, and it is current as of september 2026.

how it actually works

four steps, and only one of them is interesting.

1. capture. the app records the screen. a well built one takes the focused window rather than every pixel of every display, and skips frames when nothing changed.
2. ocr. each frame is read for text. on a mac this runs on device on the neural engine via apple's vision framework, so the words come out on your machine.
3. index. the extracted text goes into a searchable store, usually full text search plus embeddings.
4. ask. you query it, either as search or through a model that reads retrieved slices and answers.

step 4 is the only place a network is required, and only if the model is remote. the first three can all be local, which is why the architecture question is really a question about step 4. that is the argument in private ai on your mac.

what it costs in disk, measured

we measured a working install rather than estimating, because nobody publishes this.

that is roughly 1.5gb per recorded day. the ratio is the useful part: text is about 5% of the cost and video about 95%, so a year of searchable history runs around 20gb while a year of video runs around 360gb. keep text long, keep video short. full working in screen recording storage on a mac.

why the category exists

the demand is retrieval, and the supply of retrievable personal context is close to zero.

per the anthropic economic index for may 2026, the number one work task in sampled ai conversations is searching electronic sources for information at 4.95%, with reference searching second at 3.74%. that is 8.69% of conversations spent looking things up. now look at what people cannot look up.

searching your own knowledge is 3.61%. anything about a meeting is 0.26%. those are supply numbers rather than demand numbers, and screen memory exists to move them. the full read of that dataset is in what people use ai for at work.

who shipped what in 2026

every large vendor attacked this from the side it controls, and all of them stopped at their own data.

the pattern is consistent. provider memory records your conversations with one product, and platform features read the screen in the present tense. neither builds a durable, searchable archive of everything you looked at. we take the comparison apart in ai memory is everywhere in 2026, the apple case in apple intelligence on mac, and the windows one in is microsoft recall safe.

what screen memory is good at

an ai that remembers your screen answers one shape of question well, and it is always "what was that."

• "what was the number on that slide?" it was on screen, so it is text.
• "which vendor did we rule out, and why?" spread across a doc, a call and a thread.
• "find that error message from july." past the roughly 90 day browser history window.
• "what was i working on before the roadmap changed?" no single session contains a week.

the honest limit is the other half. it holds no opinions, builds no argument, and cannot tell you what mattered. for material you need to think with rather than find, writing something in your own words still beats any archive, which is the split in why you forget what you read.

what it is not

three confusions worth clearing up.

not a note taker. meeting assistants cover the 10 to 20 hours a week you are on calls and hold the audio channel only, so the number said out loud makes the transcript and the slide it was written on does not. see what ai note takers miss.

not live vision. an assistant that can see your screen right now, through screen sharing or onscreen awareness, leaves no record. it answers "what is this" and never "what was that", covered in can chatgpt see my screen.

not a second brain in the vault sense. notion, obsidian and logseq hold what you chose to file. screen memory holds what you saw. most people who take this seriously end up with both, compared in the best second brain apps in 2026.

the legal and privacy position

recording your own screen is unremarkable. recording other people's voices is not.

two party consent statutes govern conversations, meaning audio and in some states video of a conversation, rather than screen capture. so the audio setting is the one carrying real legal weight, and announcing a recording at the start of a call resolves most of it. the fuller map is in is it legal to record your screen at work, and the client work version in ai for consultants.

the privacy question is answered by architecture rather than by a policy page. where does the archive live, what can you exclude, and can you export it. be suspicious of any tool claiming nothing ever leaves your machine, because nearly every real app makes network calls for sign in, updates or crash reporting. an honest vendor scopes the claim to the archive.

regulators and researchers have both been circling this. microsoft paused recall in june 2024 after the privacy reception and spent 2 years rebuilding it, and security researchers still rate a concentrated screen archive a meaningful target no matter how good the encryption is.

the question that outlives the feature list

if the company vanished tonight, could you still open your archive tomorrow?

this category has an unusually bad record here. rewind defined it on the mac, spent 2 years telling users the archive was theirs, and in december 2025 meta acquired the company and shipped an update that disabled all capture on december 19, 2025, 14 days after the announcement, with 7 markets losing access immediately. openai's atlas browser launched in october 2025 and was retired on august 9, 2026.

so judge these tools on storage and export before features. the full argument is in what happens to your data when an ai app shuts down.

feeding it to an agent

an archive gets considerably more useful once a model can query it directly.

coding agents can read your repo and cannot see the call where the api shape was decided. connecting a screen archive over the model context protocol closes that specific gap, which is the setup in how to give an ai agent context. the wider discipline is context engineering, the retrieval mechanics are in what is rag, and the security posture you need before pointing any agent at anything is in what is prompt injection.

where remynd sits

it is the mac implementation we build, and the scope is worth stating exactly.

remynd captures the focused window, runs ocr locally so what you looked at becomes searchable text, and keeps the index on your machine. call transcription is on device too, running an mlx speech model on apple silicon. retention defaults to 30 days of recordings, you can exclude specific apps or sites from capture entirely, and your history is read only everywhere in the app.

what it does not do, stated plainly: asking a question sends the retrieved slices to a cloud model, and there is no local model option today. the in app chat ships general purpose tools, so the prompt injection caution applies to it as much as to anything else. and it does not isolate separate projects or clients into separate vaults.

that is the whole category in one page. start with how to find something you saw on your screen if you want the practical version, or what is an ai memory app for the plain english one.

download remynd for mac and stop losing the things you already saw.