Engram Improves AI Agent Memory and Efficiency

Exploring the evolving landscape of developer tools, this clip delves into a comparison between Cursor and Claude Code's CLI functionalities. A key point of discussion is how Cursor might emerge as a significant competitor to Copilot, driven by a fundamental paradigm shift in how we interact with code. The conversation extends to the broader implications of the emerging agentic era for developers, suggesting a future where AI agents play an increasingly integral role in the software development lifecycle. This shift promises to redefine productivity and innovation in the field. For more cutting edge insights from the leading builders, investors, and leaders in AI, join the Chocolate Milk Cult, the worlds best Open Source AI Research Community. #DeveloperTools #AICoding #SoftwareDevelopment #TechInnovation #AgenticAI

We can't get the most out of our Anthropic Claude Licenses - $20/month or $100/month, unless we start creating "thinking" and "self Improving" systems. The real power of tools like Claude Code lies in building structured loops: Input → Reasoning → Execution → Feedback → Memory that turn AI from a tool into a self-improving engine. The future of vibe coding isn’t faster software generation, it’s orchestrating intelligence at scale. #AI #AIArchitecture #AIDevelopment #Claude #ClaudeCode #SystemDesign #FutureOfWork #AIEngineering #Innovation

I benchmarked tokensave ( https://tokensave.dev ) against every comparable tool I came across: Dual-Graph, CodeGraph, code-review-graph, OpenWolf and more. The highlights: 37 MCP tools vs 5-22 in alternatives. 31 languages. Full call graphs, impact analysis, complexity metrics, dead code detection, type hierarchies, support for code porting. Single 25MB Rust binary, zero runtime deps, indexes thousands of files in millisecs (thank you Andrea Balducci for the challenge!). MIT licensed, every line auditable unlike some alternatives shipping proprietary cores. Almost 100M tokens saved globally with only a handful of installs. Pair it with RTK (https://lnkd.in/dpwhbw_2) as recommended by Zach Smith, a Rust CLI proxy that compresses dev tool output before it hits your context window, and the savings compounds: tokensave cuts what the AI needs to read, RTK cuts what it actually sees. Different layers, same goal.

I really wanted to share this from Enzo Lombardi I’ve been using code-review-graph and it has helped a lot, although it has been freezing often, which hurts productivity. I also experimented a bit with Caveman. Every small improvement helps I guess? Then as I was looking into OpenWolf, I came across TokenSave through Enzo's post. I also installed RTK! Another great suggestion from Enzo! Of course, proper planning and proper prompting are still the most important pieces. But Claude has become heavy on token costs. I’ve spent roughly $650 CAD this month developing my platform. It has been a major learning experience. Anthropic is seriously getting greedy. I've also used GLP5.1, Kimi2.6 and a few other open source models, I am seriously leaning away from Claude other than using Claude Code as a harness. My project, which I haven’t revealed yet, started at the end of March right as I got laid off, as an attempt to solve a personal problem. My hope is that it eventually becomes something others can benefit from as well. The biggest lesson so far: building something for personal use is one thing. Building something at a multi-user platform scale is completely different. A scaleable platform and use has so many layers. One of my goals is to minimize token and LLM usage wherever possible by using scripted APIs, automation, and deterministic logic instead of relying on prompts for everything. Operational cost of such a thing has to be accounted for. LLMs have their place. They are powerful when used properly. But some things should not be handled through an LLM or prompt at all. Nonetheless, I hope this helps everyone. #AI #AIDevelopment #LLM #Startup #FounderJourney

What if every AI app you built didn't have to reinvent agent coordination from scratch? That question led me to build the coordination layer first. It's a plug-and-play AI orchestration system with a factory at its core. It picks the right framework for the problem — currently LangGraph and AutoGen, with more specialised frameworks being integrated — spawns the right agents, runs them, and tears them down. For workflow problems: agents get wired into a graph and executed. For reasoning problems: agents debate across multiple rounds until a conclusion is reached. MCP (Model Context Protocol) servers are supported out of the box — giving agents access to external tools and context beyond what the model alone can do. The roadmap includes dynamic MCP configuration via API, so apps can define their own server needs at runtime. A FastAPI backend exposes it all as APIs — input parameters control whether you get just the output or the full agent communication trace. This is the foundation. Everything I build next plugs into this as its AI backbone. GitHub 👇 https://lnkd.in/gxNwtpPG --- Shoutout to Ed Donner — his courses were instrumental in shaping how I approached this. And credit to the Claude VS Code extension for coding assistance throughout — a tool that genuinely made the process faster without compromising on quality. #AI #AgentOrchestration #MCP #ModelContextProtocol #BuildInPublic #LangGraph #AutoGen #FastAPI #SoftwareEngineering

DeepSeek Releases V4 Preview to Boost Open-Source Coding and Reasoning 📌 DeepSeek has unveiled its powerful V4 series, a new generation of Mixture-of-Experts models designed to rival industry leaders in coding and mathematical reasoning. Featuring massive parameter counts and a massive 1M token context window, the release includes high-efficiency options like DeepSeek-V4-Flash for economical enterprise deployment. This breakthrough marks a major leap for open-weight AI, setting new benchmarks for performance and accessibility in software engineering. 🔗 Read more: https://lnkd.in/deyRpBdB #Deepseekv4 #Mixtureofexperts #Largelanguagemodels #Opensourcecoding

I almost broke my own AI system… 10 minutes before demo. Everything was working fine—until it wasn’t. Streaming froze. Agent loop went crazy. RAG returned garbage. For a moment, I thought… this is it. But that’s the thing about building real AI systems— it’s not about perfect demos, it’s about handling chaos in production. So I fixed it. Optimized retries. Tightened prompts. Controlled agent flow. And what came out of that breakdown is something I’m genuinely proud of 👇 Built a production-ready AI microservice using: • LangChain + FastAPI • Agent-based tool usage (with trace) • RAG with FAISS (PDF + TXT ingestion) • Conversation memory • Versioned prompts • Streaming responses • Retry + fallback mechanisms • Full observability (LangSmith + Phoenix) This isn’t just another “chatbot”. This is how real AI systems are built to survive production. Dropping the demo video next Would love your honest feedback. Github Repo - https://lnkd.in/dxDgJkie #AIEngineer #GenAI #LangChain #RAG #FastAPI #BuildInPublic #AIProducts

GitNexus Launches Open-Source Knowledge Graph Engine for AI Agent Codebase Awareness 📌 GitNexus delivers a revolutionary open-source knowledge graph engine that lets AI coding agents truly understand codebases-no more guesswork or fragmented context. By pre-building dependency maps via ASTs and community detection, it empowers tools like Cursor and Claude Code to make confident, coordinated edits. MCP-native integration means smarter, safer refactoring with zero server overhead-finally giving AI agents the architectural awareness they need. 🔗 Read more: https://lnkd.in/dr6mm7en #Gitnexus #Knowledgegraph #Codebaseawareness #Aiagents #Mcpnative

🚀 Excited to share that I’ve officially open-sourced my project: AI Context Scraper (Apify)! 🔗 GitHub: https://lnkd.in/guVfkyqb This project is designed to intelligently extract and structure contextual data from the web, making it easier to feed high-quality inputs into AI systems, workflows, and automation pipelines. 💡 What it does: 1) Scrapes and structures relevant context from web sources 2) Optimized for AI/LLM use cases (prompt enrichment, data pipelines, etc.) 3) Built using Apify actors for scalable and reliable scraping ⚙️ Deployment: The project is still live and running on Apify, enabling real-time usage and testing. 🎯 Why I built this: While working on AI workflows, I realized that context quality = output quality. This tool helps bridge that gap by automating context collection in a clean, usable format. 🔍 Use cases: AI agents & automation workflows Data collection for ML/LLM pipelines Context augmentation for better model responses Would love feedback, contributions, or ideas on how to improve this further! #OpenSource #AI #MachineLearning #Apify #WebScraping #LLM #Automation #DataEngineering

If you’re just plugging a single API key into a prompt and calling it an "agentic backend," you aren't building a product. You’re building a liability. The future of development isn't about finding the "best" model. It's about Model Orchestration. Engineering in the AI era has to include Token Economics. It’s the difference between a prototype that works and a production system that’s sustainable. Different models and platforms for different use cases: 1. Local (The Zero-Marginal-Cost Layer) Using things like Exo clusters/Ollama/LM Studio for local inference. 👉 Use case: High-volume, low-complexity tasks, PII scrubbing, or "rough draft" PoC iterations. 👉 The Win: Once the hardware is paid for, your marginal cost per token is effectively zero. 2. OpenRouter (The Agility Layer) The "Switzerland" of LLMs. 👉 Use case: Rapid prototyping and fallback routing. 👉 The Win: You can swap a model in one line of code. If a provider goes down or a new "SOTA" model drops on a Tuesday, your backend doesn't need a rebuild: just a config change. You can test new models as they come out. Qwen3.5 was the new tool everyone wanted 3 weeks ago, now it's Gemma-4. 3. Frontier/Direct (The Intelligence Ceiling) Going straight to Anthropic or OpenAI for the heavy lifting. 👉 Use case: Complex reasoning, final-pass quality control, and high-stakes decision making. 👉 The Win: You get the absolute maximum "intelligence" available, but you pay the premium for it. The Takeaway: The goal isn't to pick one. The goal is Token Prediction. Before a feature ships, you should be able to calculate: <Calls per Action> × <Avg Tokens> × <Model Cost> × <Projected Volume> = <Monthly Burn>. Stop automatically switching to Opus at the first opportunity and remember that you should be choosing the right tool for the job. #AI #LLM #SoftwareEngineering #AgenticWorkflows #TokenEconomics #DeveloperExperience