标签：#RAM

Most AI coding assistants do not really remember your project . They remember just enough to be dangerous . They see the latest prompt, skim a few files, improvise, and then forget the reasoning that made the answer useful five minutes ago. That is fine for toy demos. It breaks down fast inside a real software codebase. In Knotic I take an harder line . Instead of treating memory like a chat log with extra lipstick, I treat memory as infrastructure . Project knowledge is separated from session knowledge. Source material is separated from condensed understanding . Old context is compressed instead of blindly dragged forward. The result is a system that feels less like autocomplete with a caffeine habit and more like an AI engineering partner that can stay oriented over time. If you care about AI coding assistant memory , context engineering , persistent project memory , or long-term memory for software development , this is the part worth paying attention to. The Real Problem With AI Memory in Coding Tools The average AI IDE has the same failure mode . It looks smart on the first turn and shaky on the fifth . Why? Because software work is not just about answering the latest question. It is about carrying forward constraints, architecture, naming conventions, decisions, tradeoffs, dead ends, file relationships , and the exact context of the change in progress. When an assistant does not separate those layers, everything gets mixed together . Stable project facts sit next to temporary tool output. Important decisions compete with random noise. The model burns tokens re-reading the same files, or worse, works from partial memory and starts making up the missing pieces . Knotic solves this by splitting memory into distinct layers , each with a clear job. That design choice sounds simple. In practice, it changes everything . Knotic Does Not Use One Memory. It Uses Three. Knotic's memory model is built around three different kinds of context . The first is long-term projec

I've been an engineer for almost 9 years, and I know from experience how much coding has changed over the years. Right now Im working in a big blockchain company and honestly I feel pretty exhausted. BUT NOT FROM THE TASKS I EXECUTE. I think with AI now, my work is more like being a human API. Lol. I got to slack, emails, JIRA and zoom calls to interact with people and gather all the context needed in order to make sure that when I will use AI the results will be relevant and accurate. And I feel that this is actually draining me. And i realized that this because every time we open PRs and it is about time to review things, CIs are freaking failing everywhere and then I have to go back and forth with people on slack to get the missing context. And all that even if we have already done scoping, architectural decisions. I feel we rush so much to deliver things fast, due to the AI-speed pressure, that is causing all this. I actually found many articles online talking about this. Anthropic also did their own index for checking if the fatigue is real from AI usage. I linked a medium article that resonated with me on the topic. Are you also facing this issue at your job? If so, how are you dealing with this, apart from taking more walks at the park lol. submitted by /u/LeopardAfter493 [link] [留言]

Simon Brown explains that the C4 Model started not as a grand design theory, but as a practical answer to an embarrassing problem. Furthermore, he answers the question on how to handle microservices in C4 and explains the important distinction between modeling and diagramming. submitted by /u/goto-con [link] [留言]

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This article covers Node.js garbage collection (Mark-and-Sweep), memory leaks, and practical techniques for debugging them using Clinic.js, heap dumps, and heap snapshot comparisons. submitted by /u/cryptomallu123 [link] [留言]

As announced in this blog post on June 18, 2026, Gemini CLI and Gemini Code Assist IDE extensions...

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Originally published on lavkesh.com I recall one of the first design decisions for our payments platform, which was to deploy an in-memory cache for low-latency access to customer account balances. The architecture diagrams looked clean, with Go services using sync.Once-initialized maps in memory, bypassing the database for sub-millisecond reads. However, this approach worked as expected for only three months, until users started reporting inconsistent charges on receipts. The problem surfaced at peak hours when concurrent updates to the same balance would overwrite each other. For instance, the account balance for user ID 12345 went from $1,200 to $850 to $1,200 again within seconds, leaving the cache in a state that defied the database of record. Engineers stared at the logs, baffled by the mismatch between transactions and cached values, because the team had not accounted for the fact that memory maps are not thread-safe by default in Go. Debugging revealed the fundamental error: we were optimizing for speed without considering write-through guarantees. The cache treated concurrent requests as idempotent, which they were not. During a single user’s purchase flow, multiple goroutines could validate the balance, each reading a stale value from memory before any had a chance to commit updates. We had to shift to Redis with explicit lock keys and time-to-live settings, adding 4 milliseconds of latency but ensuring atomicity. The cache also invalidated itself only when a change occurred, not when an upstream source updated. We discovered this when the accounting team reconciled overnight and adjusted balances based on fee settlements - the cache never reflected these updates until it expired naturally. To fix this, we had to implement message queues to broadcast invalidation events across all services. What started as a performance optimization became three nights’ worth of rewriting concurrency models. This experience taught me two concrete lessons about distributed

You think your AI is just helping you write code. In reality, it's built a logging system on your machine that you never knew existed. Every conversation. Every code snippet. Every file path. Every time you asked "what was my password again?" — permanently archived, without your knowledge. How It Started: An Accidental Discovery I was about to sell my old laptop and decided to clean up my data first. I opened Claude Code's config directory — ~/.claude/ — intending to just remove my API key. Then I saw this: history.jsonl 243 KB / 695 lines sessions/ conversation metadata session-env/ environment variables shell-snapshots/ command execution snapshots telemetry/ 63 telemetry files projects/ 19 project directories ├─ interview-prep/ 31 sessions / 20 MB ├─ spring-ai/ 11 sessions / 13 MB └─ ... 17 more I thought I was just writing code. My computer thought it should record everything. What's Inside These Files history.jsonl — Everything You Ever Asked 695 entries. Every single thing I typed into Claude Code. Including: "I forgot my database password — can you check what passwords were configured in the project files?" "How do I view the database password?" Pasted code snippets Every /model , "who are you?", and project path You casually ask about a password once. It's permanently stored. projects/ — Full Conversation Transcripts (43 MB) If you think history.jsonl only storing user input isn't so bad — you haven't seen this yet. Inside ~/.claude/projects/ , every project directory contains .jsonl files. Opening one 2.3 MB session file: Content Count AI responses 590 AI internal thinking blocks 272 Tool calls 101 Tool call results (including file paths) 100 File history snapshots 208 Every conversation. Every AI response. Every internal reasoning step. Every file operation — what was read, what was modified, what was executed — all written to this file. shell-snapshots/ — Traces of Everything You Ran Your system PATH. Installed tools. Java version. All sitting in command s

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The Prizes for the SecDim AppSec Village Wargame CtF at DEF CON 34 has been announced! Build and submit a challenge and get a chance to Win a ROG Xbox Ally if your challenge submission wins 1st Place! Challenge submissions close on 31 July. https://sessionize.com/appsec-village-wargame

𝗦𝗮𝘆 𝗵𝗲𝗹𝗹𝗼 𝘁𝗼 𝗟𝘂𝗺𝗼𝗿𝗮 — 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁. 💎 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: https://github.com/Chetankumar-Akarte/lumora 🔗 Demo: https://renukatechnologies.in/demo/lumora/ Don't forgot to 🤩 Star and 👉 Fork the Repo 𝗟𝘂𝗺𝗼𝗿𝗮 is a modern, responsive 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁 designed for teams that need a polished, enterprise-ready control center without the bloat. Whether you are building for SaaS, CRM, E-commerce, or internal analytics, Lumora provides a scalable, token-driven foundation to speed up your workflow. 𝗟𝘂𝗺𝗼𝗿𝗮 is the result: a complete admin ecosystem featuring everything from KPI blocks and ApexCharts to full E-commerce management flows and authentication screens. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Full UI Kit with basic and advanced components. • Enterprise pages (Users, Roles, Permissions, Invoices). • Interactive apps like Calendar and Contacts. • Clean, token-driven styling for consistent design. 𝗧𝗲𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • Bootstrap 5.3 • ApexCharts & Chart.js • Vanilla JavaScript • Mobile-first design 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Built with Bootstrap 5.3, Vanilla JS, and CSS3 using a module-first architecture. • 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Includes layouts for Analytics, CRM, Project Management, HRM, and more. • 𝗙𝗲𝗮𝘁𝘂𝗿𝗲-𝗣𝗮𝗰𝗸𝗲𝗱 𝗔𝗽𝗽𝘀: Ready-to-use interfaces for Advanced Chat, Kanban boards, Email, and File Management. • 𝗗𝗮𝗿𝗸 & 𝗟𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝘀: Clean, professional visuals with seamless theme switching. • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: Modular CSS, reusable partials, and organized project structure. I built this to bridge the gap between "pretty" templates and "functional" enterprise tools. Check it out, star the repo, and let me know what you think! I'd love for you to take a look at the code and perhaps even use it for your next project. Feedback and contributions are always welcome! WebDevelopment, Bootstrap5, AdminDashboard, OpenSource, UIUX, JavaScript, GitHub, Bootstrap, CodingCommunity, OpenSourceProject, FrontendDev, LumoraUI

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📚 Limn Engine — Complete API Reference Quick Navigation Class Purpose Level Display Canvas, game loop, input, camera, scenes 🟢 L1 Component Every visible game object 🟢 L1 Camera Viewport control (follow, shake, zoom) 🟡 L2 move Movement, physics, particles, helpers 🟢 L1 state Read-only query helpers 🟢 L1 TileMap Grid-based levels 🟡 L2 Tctxt Rich text with backgrounds 🟢 L1 Sound Single audio file 🟢 L1 SoundManager Multiple sounds, volume control 🔴 L4 ParticleSystem Emit, burst, continuous emitters 🟠 L3 Sprite Spritesheet animation 🟡 L2 Display The heart of every Limn Engine game. Creates the canvas, runs the game loop, captures input, manages the camera, and controls scenes. Constructor const display = new Display (); Properties Property Type Description .canvas HTMLCanvasElement The game canvas .context CanvasRenderingContext2D 2D drawing context .keys Array Boolean array indexed by keyCode .scene Number Current active scene (default 0) .camera Camera Attached camera instance .deltaTime Number Time since last frame (seconds) .fps Number Current frames per second .frameNo Number Total frames elapsed .x / .y Number false Methods Method Parameters Description .start(w, h, node) width, height, parentNode Initialise canvas and start game loop .perform() — Activate dual-canvas pipeline (call before .start() ) .add(comp, scene) Component, scene number Register a Component for rendering .stop() — Pause the game loop .scale(w, h) width, height Resize canvas after start .backgroundColor(color) CSS color Set background colour .lgradient(dir, c1, c2) direction, color, color Linear gradient background .rgradient(c1, c2) color, color Radial gradient background .fullScreen() — Enter fullscreen .exitScreen() — Exit fullscreen .tileMap() — Build TileMap from display.map and display.tile Usage const display = new Display (); display . perform (); display . start ( 800 , 600 ); display . backgroundColor ( " #0a0a2a " ); const player = new Component ( 40 , 40 , " blue " , 100 , 100 ); d

Hi folks, I wrote a paper, Fearless Concurrency on the GPU, and maintain the related repository cuTile Rust ( https://github.com/nvlabs/cutile-rs ). The idea is to establish a safe way to write async kernel launch code, extend that across the kernel launch boundary, and sustain (to the extent possible) a safe programming model for GPU programming in Rust. We provide a variety of tools to enable static bounds checks so that the data-race freedom is effectively zero-cost. Sharing in case it's of interest. Happy to answer questions. submitted by /u/Exciting_Suspect9088 [link] [留言]

I promised myself that starting this week I'd switch to lighter topics. But on Monday, my JSNation...