Product preview: the DailyAi.Work memory workspace. A search for “Snowflake incremental load” returns saved answers from Claude, ChatGPT, and Gemini, labelled as AI-memory or keyword matches. The selected answer shows its source model and conversation, and offers to reuse it in Gemini.
Use a stream on the raw table and a scheduled task that runs a MERGE — only changed rows are processed on each incremental load.
ClaudeData Engineering· Oct 2
MERGE pattern for late-arriving rows
Keyword
Deduplicate with QUALIFY ROW_NUMBER() before the merge so retries stay idempotent.
ChatGPTData Engineering· Sep 26
dbt incremental model with is_incremental()
AI memory
Filter on the max loaded timestamp so each run only scans new partitions.
GeminiLearning· Sep 14
Memory
Incremental loads with Snowflake streams + tasks
MERGE INTO orders t
USING orders_stream s
ON t.id = s.id
WHEN MATCHED THEN UPDATE …
WHEN NOT MATCHED THEN INSERT …
Source
Model
Claude
Chat
Snowflake CDC pipeline design
Saved
Oct 2 · Data Engineering
Open original conversation
Reuse in
ChatGPTClaudeGemini
Send to Gemini
Free — no credits used
Works alongside
ChatGPT
Claude
Gemini
Grok
Perplexity
Your existing accounts, unchanged. Not affiliated with any provider.
Free plan available
No credit card required
Works alongside your existing LLM accounts
Chrome extension
Built for AI-power users
Local-first capture
Your data stays under your control
MCP-ready
The problem
Your best AI answers shouldn't disappear.
You use several LLMs every day. The useful parts — code, SQL, decisions, research — end up scattered across hundreds of chats you'll never scroll back through.
Answers you can't find again
You ask ChatGPT something useful, then can't find it next week. Chat history search wasn't built for knowledge.
Code fixes buried in old chats
Claude gives you a great fix, but it stays buried in a thread you'll never scroll back to.
Research scattered across tabs
Gemini helps with research, but the insights live in five different chats across three apps.
The same prompt, rewritten again
You rewrite the same prompt every week, and comparing models means copy-pasting between tabs.
How it works
DailyAi.Work turns LLM conversations into your personal AI memory.
Install the extension, keep using your favorite LLM, and let the useful parts add up instead of disappearing.
Save a full chat, a single response, or just the code block that worked. The side panel sits next to the conversation, so saving takes a click, not a copy-paste.
supported LLM sites
5
supported LLM sites
capture types
4
capture types
Save chat or save response from the side panel
Capture answers, prompts, code, and decisions
Source model and conversation kept with every capture
Illustration: an LLM chat with one answer selected. A “Save to memory” card offers capture types — answer, prompt, code, decision — with Answer selected, the Data Engineering project, suggested tags, and a Save memory button.
your-llm-chat.example/c/cdc-pipeline
How do I load only changed rows from our orders table into Snowflake?
Create a stream on the raw table, then schedule a task that merges it:
CREATE STREAM orders_stream ON TABLE raw.orders;
DailyAi.WorkSave to memory
AnswerPromptCodeDecision
Data Engineering
#snowflake#cdc#mergesuggested
Save memory
Organize
Memories, grouped the way you work.
File captures into projects like Data Engineering, Product Launch, or Learning. Tags and topics keep related answers together, whichever model they came from.
for every workstream
Projects
for every workstream
and topics, AI-suggested
Tags
and topics, AI-suggested
Group chats, prompts, snippets, and decisions by project
AI summaries and auto-tags (uses credits)
Pin, tag, and bulk-select captures
Illustration: three project boards — Data Engineering, Product Launch, and Learning — each listing saved memories with their source model. One memory shows AI-suggested tags.
Data Engineering
24 memories
Streams + tasks MERGE
Claudetagged
Late-arriving rows dedup
ChatGPT
Clustering key choice
Gemini
Product Launch
11 memories
Launch checklist draft
ChatGPT
Pricing page copy review
Claude
Learning
17 memories
dbt incremental models
Gemini
MCP server basics
Claude
Find
Search every model's answers at once.
Keyword search finds the exact phrase. AI-memory search finds the answer even when you don't remember the words. Every result shows its source model and conversation.
search modes: keyword + semantic
2
search modes: keyword + semantic
search box for every provider
1
search box for every provider
Keyword and AI-memory results are labelled separately
Snippets highlight the matching passage
Open the original conversation in one click
Illustration: a search for “Snowflake incremental load” with AI-memory and keyword tabs. The top result, an AI-memory match from Claude, is highlighted, and a source card shows the original conversation, project, and date with a link to open it.
Snowflake incremental load
AI memory · 2Keyword · 1
Incremental loads with Snowflake streams + tasks
…a scheduled task runs a MERGE so each incremental load only touches changed rows…
dbt incremental model with is_incremental()
Source
Snowflake CDC pipeline design
ClaudeOct 2 · Data Engineering
Open original conversation
Reuse
Start the next chat with the right context.
Open a saved memory and carry it into a different model — paste it as context, run it through a prompt with variables, or compare how another model answers.
prompt variables
{{ }}
prompt variables
export formats
5
export formats
Send a memory or prompt to ChatGPT, Claude, or Gemini
Fill variables like {{table}} and {{dialect}} before you run
Export as Markdown, TXT, JSON, CSV, or PDF
Free plan available. No credit card required.
Illustration: a saved Claude memory feeding a prompt with variables table equals orders and dialect equals Snowflake. Gemini is selected as the target model, with Send to Gemini and Compare with Claude buttons. The action is marked free, with no credits used.
Incremental loads with Snowflake streams + tasks
ClaudeMemory · Data Engineering
Prompt · Review an incremental load
2 variables
Using the saved context, review the incremental load for {{table}} in {{dialect}} and flag edge cases.
tableordersdialectSnowflake
Send toChatGPTClaudeGemini
Send to GeminiCompare with Claude
Free — no credits used
Ask
Ask your AI memory anything.
The in-memory chatbot answers from what you've saved — and cites the original conversations, so you can always check the source.
What did Claude suggest for optimizing this Snowflake query?
Find the SQL schema I discussed last week.
Summarize my research on MCP servers.
What action items came from my recent code-review chats?
Illustration: the memory chatbot answering a question about a Snowflake query, citing two saved Claude conversations as sources with links back to the originals.
What did Claude suggest for optimizing this Snowflake query?
Cluster the table on event_date and filter on it before the join, so pruning skips most micro-partitions1. It also suggested replacing DISTINCT with a QUALIFY window2.
1Snowflake pruning deep-diveClaudeOct 2
2Dedup patterns in SQLClaudeSep 19
Ask your AI memory…Uses credits
Act
From AI answer to completed task.
Pull an action item out of a chat, send it to an LLM, capture the response, and track it to done. The final answer stays linked to the conversation it came from.
Extract an action item from an LLM chat
Send it to ChatGPT, Claude, Gemini, or Grok
Capture the response and track created → completed
Keep the final answer as reusable memory
Illustration: a TODO list with three items linked to their source chats — one completed, one in progress, one created. The in-progress item shows it was sent to Claude and the response was captured.
TODOs
Data Engineering
Add composite index to events table
From: Postgres index strategy
Completed
Quarantine flaky checkout spec
From: Playwright triage
In progress
Sent toClaude Response captured
Draft MCP server rollout plan
From: MCP research notes
Created
MCP server · Pro
Connect your AI memory to any MCP-compatible agent.
Your saved knowledge shouldn't be locked in one app. The DailyAi.Work MCP server makes it available to the agents and tools you already use.
Expose saved chats, snippets, projects, and workflows through MCP
Let Claude Desktop, Cursor, or other MCP clients search your memory
Send summaries and project context to the models you use
Use reusable MCP prompts and workflows
Diagram: DailyAi.Work memory at the center, exposing chats, snippets, projects, and workflows over MCP to Claude Desktop, Cursor, your own agents, and any other MCP client.
Claude DesktopCursorYour agentsAny MCP client
DailyAi.Work memory
MCP server
ChatsSnippetsProjectsWorkflows
Features
Everything you need to make AI answers reusable
An AI workspace and ChatGPT, Claude, and Gemini organizer in one browser extension — with a prompt library, AI memory search, workflows, and an MCP server.
Capture
Multi-LLM capture
Save chats, responses, prompts, code, and highlights from ChatGPT, Claude, Gemini, Grok, Perplexity, and more.
Capture
Snippets & highlights
Keep the best answers, code blocks, SQL queries, and decisions ready to reuse.
Organize
Unified AI memory
Search across every saved LLM conversation from one workspace.
Reuse
Prompt variables
Use variables like {{language}}, {{code}}, {{topic}}, and {{context}} to reuse prompts faster.
Explain {{code}} in {{language}} for a reader new to {{topic}}. Use {{context}}.
Reuse
Prompt library
Save reusable prompts with folders, tags, favorites, and version history.
Organize
Projects
Group chats, prompts, snippets, files, and decisions by project.
Find
AI memory search
Keyword and semantic search find the right answer, even when you don't remember the exact words.
Find
In-memory chatbot
Ask questions about your saved AI knowledge and get answers that cite their sources.
Reuse
Prompt launcher
Launch or copy a prompt straight into ChatGPT, Claude, Gemini, Grok, or another supported LLM.
Reuse
Export anywhere
Export chats, snippets, projects, and workflows as Markdown, TXT, JSON, CSV, or PDF.
.md
.txt
.json
.csv
.pdf
Free: .md and .txt
Find
New Tab command center
Recent captures, projects, prompts, and quick-launch links in every new tab.
Automate
TODO tracking
Turn AI recommendations into TODOs, send them to an LLM, capture the response, and mark them complete.
Automate
Agent workflows
Run multi-step workflows for research, code review, SQL design, debugging, and project recaps.
Automate
Usage & credits
See the estimated credit cost before every AI action, and track usage by action, provider, and project.
Credits this month640 / 1,000
Estimated cost shown before every AI action.
ProAutomate
MCP server
Connect your DailyAi.Work memory to MCP-compatible AI agents and tools.
Free plan available. No credit card required.
Pricing
Start free. Upgrade when it pays for itself.
Credits are used for AI-powered actions, and every action shows its estimated cost before it runs.
Free
Everything you need to start building your AI memory.
$0/month
ChatGPT + one additional LLM provider
50 saved chats
3 projects/folders
10 saved prompts
Prompt variables
30 prompt runs/month
Basic keyword search
10 semantic memory searches/month
10 AI summaries/auto-tags/month
1 workflow with 10 runs/month
5 exports/month
Markdown/TXT export
50 AI credits/month
Basic New Tab helper
Recommended
Pro
For people who run real work through LLMs every day.