Sharing a quick breakdown of something I recently built that dramatically improved long-session performance in LLM-based agents. The challenge => Most chatbots forget everything between sessions — or worse, overload memory with raw logs that aren’t meaningful. My solution => a structured memory system that’s adaptive, contextual, and forgets what it should. Here’s the architecture: 1. Memory Points - Each user message is turned into a memory point with: - A content summary - A dynamic importance score - A category (like “personal info” or “casual”) - A timestamp - Linked to a user ID 2. Memory Decay - Memories fade over time — but not equally. - Critical info (like names or settings) decays slowly - Small talk fades fast I use exponential decay with category-based rates. 3. Long-Term Summarization - When a memory fades below a threshold, the LLM generates a long-term summary (e.g., "User likes dark mode"). These are stored as part of the user’s evolving profile. 4. Contextual Retrieval & Conflict Handling - At inference time, the bot pulls in recent memory points + long-term memory. If there are conflicts, it resolves them based on score, recency, and category. Why it matters:It creates conversations that feel personalized and consistent, without storing everything. It’s lean, adaptive, and avoids token overload. If anyone else here is building AI agents or tools around personalization/memory — happy to trade notes or dive deeper!

Anthropic just released the next generation of Claude models: Claude Opus 4 and Claude Sonnet 4! The models achieve SOTA performance in SWE-bench with a score of 72.5% for Opus and 72.7% for Sonnet Along with the model release, the models get a bunch of new abilities and features: - Extended thinking with tool use (beta): Both models can use tools—like web search—during extended thinking so the models can to alternate between reasoning and tool use to provide way better responses - Parallel tool execution: Both models can now use tools in parallel - Improved prompt adherence: The models now follow instructions more precisely than previous models - Better memory capabilities: The new models are better at extracting and saving key facts to maintain continuity and build tacit knowledge over time The pricing for the models remains consistent with previous Opus and Sonnet models: Opus 4 at $15/$75 per million tokens (input/output) and Sonnet 4 at $3/$15. Both models are available on the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI. The Anthropic API also received some updates that should help build more powerful AI agents: - code execution tool - MCP connector - Files API - caching prompts for up to one hour Lastly, Claude Code is now generally available and it now supports background tasks via GitHub Actions and native integrations with VS Code and JetBrains. The models are already available in AI IDEs such as Cursor and GitHub Copilot. Read the full announcement post here: https://www.anthropic.com/news/claude-4

I picked up this piece, what would happen if you put your features in a 2-year circle nowadays 🤔 would you survive with the best software being developed every day? Obviously it's a big no, but here is my take "Moving fast and addressing the problem, wouldn't put you at risk" Also setting a time frame to marge codes I feel its a best practice not really sure how long that should be but 7 to 14 days for me is best - I happen to change my mind and remove some features 😉 either way, @Raymond Adeniyi @Digitl-Alchemyst Steven-Watkins what's your views?