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Tencent just open-sourced Tencent DB Agent Memory, a clever local AI memory system that mimics human-like recall. It boosts AI agent performance by over 50% and cuts costs by 61%, making agents smarter and more personal over time. #tencentdb #aimemory #aitechnology #opensourceai #smartagents (00:00) Tencent (00:01) TencentDB (00:01) Agent Memory (00:02) Agents remember. Humans innovate. (00:02) Migrating data from an older version (00:03) If you're already on an older release (v1.x / v0.x) and want to bring your existing data over to v2.0.0+, we provide a migration tool: (00:03) see INSTALL.md (中文: INSTALL_CN.md). (00:04) UNLIMITED (00:04) AI MEMORY (00:05) memory (00:05) Claude Code (00:06) any AI agent (00:07) Tired of repeating yourself? Tell Claude to (00:08) remember what you've told it using (00:08) CLAUDE.md. (00:09) Prefer the Terminal experience? Switch back in Settings. (00:10) .neumorphic-button:after { (00:10) content: "; (00:11) @apply absolute inset-0 opacity-0 transition-opacity duration-300 bg-gradient-to-br (00:11) [@968f7f]/20 to-transparent rounded-full; (00:12) } (00:12) .neumorphic-button:hover:after { (00:13) @apply opacity-100; (00:13) } (00:13) ... (00:14) Claude Code v2.1.160 (00:14) Opus 4.8 (1M context) with medium effort - Claude Pro (00:15) /Users/josephodoro (00:15) /fast (00:16) /focus (00:16) /framer (00:17) /feedback (00:17) /figma-use (00:18) /firecrawl (00:18) /framer-audit (00:19) /firecrawl-map (00:19) Toggle fast mode (Opus (00:20) Toggle focus view (show (00:20) summary, and the final (00:21) Use when the user wants to (00:21) publish a website or app (00:22) Submit feedback, report (00:22) conversation (00:23) (figma) MANDATORY: perform (00:23) (figma) invoke this skill BEFORE (00:24) Search, scrape, and import (00:24) Firecrawl CLI. Use this to (00:25) Run a batch of audits for (00:25) health, CMS integrity, SEO. (00:26) Discover and list all (00:26) Claude Website (00:27) Claude Website (00:27) When chat message (00:28) received (00:28) Get Memories (00:29) Search Record (00:29) Aggregate (00:30) Merge (00:30) combine (00:30) Editor (00:31) Executions (00:31) Agent (00:32) Style (00:32) New Chat (00:33) Ask Framer... (00:33) Claude Code v2.1.160 (00:34) Tips for getting started (00:34) Run/limit to create a CLAUDE (00:35) No recent activity (00:35) Recent activity (00:36) - Let me read the most relevant articles to answer this. (00:36) - Read 10 files (ctrl+o to expand) (00:37) - Your biggest inspiration, based on what the wiki documents, is Naruto -- and it's (00:37) in terms of depth and duration of influence. (00:38) > bypass Permissions on (shift+tab to Cycle) - esc to Interrupt (00:38) Good afternoon, team vm (00:39) How do I manage all my documentation? (00:39) Sonnet 4.6 (00:40) UNLIMITED (00:40) AI MEMORY (00:41) Close it (00:41) a new one (00:42) no idea (00:42) you are (00:43) prefer (00:43) you were (00:44) last week (00:44) So Tencent (00:45) tier (00:45) system (00:46) completely (00:46) of how (00:47) works (00:47) Analyze this document (00:48) You uploaded a document (00:48) Q2 Risk Assessment Report.pdf (00:49) PDF 1.2 MB (00:49) Now summarize only the risks (00:50) AI Assistant (00:50) Here are the key risks identified in the document: (00:51) • Market volatility could impact revenue projections. (00:51) • Supply chain disruptions may delay critical deliveries. (00:52) • Regulatory changes may increase compliance costs. (00:52) • Cybersecurity threats pose a risk to sensitive data. (00:53) • Talent retention challenges could affect key initiatives. (00:53) memory (00:54) happening (00:54) Long term (00:55) stores (00:55) preferences (00:56) personality (00:56) over time (00:57) works (00:57) It watches (00:58) conversations (00:58) pulls out (00:59) like (00:59) prefers (01:00) Python or (01:00) wants (01:01) responses (01:01) and stores (01:02) that get (01:02) the longer (01:03) the agent (01:03) token (01:04) by 61% (01:04) task (01:05) because (01:05) Rereading (01:06) background (01:06) session (01:07) source and (01:07) locally (01:08) Memory (01:08) Capability (01:09) Benchmark (01:09) OpenClaw (01:10) Success (01:10) With (01:11) Plugin (01:11) Relativ (01:12) Δ (01:12) Short-term (01:13) WideSearch (01:13) 33% (01:14) 50% (01:14) +51.52% (01:15) Short-term (01:15) SWE-bench (01:16) 58.4% (01:16) 64.2% (01:17) +9.93% (01:17) Short-term (01:18) AA-LCR (01:18) 44.0% (01:19) 47.5% (01:19) +7.95% (01:20) Long-term (01:20) PersonaMem (01:21) 48% (01:21) 76% (01:22) +59% (01:22) These results are measured over continuous long-horizon (01:23) sessions, not isolated turns. For example, SWE-bench runs (01:23) 50 consecutive tasks per session to simulate the context- (01:24) accumulation pressure of real-world long-horizon agents. (01:24) Overview